shear rate, γ γγ γ
η ηη η
.
Figure 2.1.: Deformation of an ideal ﬂuid exhibiting a Newtonian behaviour (Hackley
and Ferraris, 2001)
˙ γ =
∂γ
∂t
(2.4)
The simplest Newtonian behaviour described by equation (2.1) is analogous to the
Hookean behaviour of an elastic solid. In an elastic solid material, shear strain is
proportional to applied shear stress and if stress is removed, the solid material fully
recovers the initial shape.
For many ﬂuids, like polymers, paints, cosmetics, cement paste and concrete the re-
lationship between shear stress and shear rate is non-linear (it can be shear-thinning
or shear-thickening). Moreover, for some ﬂuids, ﬂow only initiates after a certain level
of stress is surpassed (called the yield stress). These rheological behaviours, diﬀerent
from the Newtonian, will be further discussed in 2.2.2. Some materials also have a
time dependence due to a ﬂocculated microstructure, where the ﬂow characteristics are
inﬂuenced by the shear history of a material (it can be thixotropic or anti-thixotropic).
This will be also discussed in 2.2.2.
As can be observed in Table 2.1 the viscosity of diﬀerent materials varies in a wide range
as well as the shear rate range associated to diﬀerent industrial processes. Moreover, the
viscosity may vary with applied shear rate, as in the case of non-Newtonian materials.
Thus, to be able to characterize a ﬂuid, a suitable instrument and measuring technique
must be selected. In Section 2.3, a review of existing instruments, measuring tools and
measuring techniques is presented. A mathematical model is often required to describe
the rheological behaviour of a ﬂuid and several theoretical models can be found in the
literature. Those most often used for cementitious materials will also be presented
in 2.2.3.
10
2.2. Rheology
Table 2.1.: Typical values of viscosity for diﬀerent materials and typical shear rate as-
sociated to diﬀerent industrial processes (Chhabra and Richardson, 1999;
Esping, 2007)
Material approx. viscosity (Pa.s) Process shear rate range (s
−1
)
air 10
−5
sedimentation 10
−1
- 10
−3
water 10
−3
levelling 10
−1
- 10
−2
olive oil 10
−1
extruding 1- 10
2
mortar 1 pumping 1- 10
3
SCC 100 mixing/stirring 10
1
- 10
3
molten glass 10
15
spraying 10
4
- 10
5
2.2.2. Flow curve types
As shown in Figure 2.1 the plot of shear stress against shear rate (called ﬂow curve
or rheogram) for a Newtonian ﬂuid is a straight line of slope η passing through the
origin. The single constant η therefore completely characterises the ﬂow behaviour of
a Newtonian ﬂuid at ﬁxed temperature and pressure (Chhabra and Richardson, 1999).
A non-Newtonian ﬂuid is one whose ﬂow curve is non-linear or does not pass through
the origin, i.e. where the viscosity (shear stress divided by shear rate) is not constant
at a given temperature and pressure but is dependent on ﬂow conditions such as ﬂow
geometry, shear rate, shear history etc (Chhabra and Richardson, 1999).
Time independent ﬂow behaviour
The non-newtonian ﬂuids for which the shear rate at any point is determined only by the
value of the shear stress at that point, at that instant, are known as time-independent.
These ﬂuids may be further subdivided into three types: shear-thinning (or pseudo-
plastic); shear-thickening (or dilatant); and viscoplastic. Qualitative ﬂow curves for
these types of ﬂuid behaviour and corresponding variation of viscosity with shear rate
are shown in Figure 2.2.
In all types of non-Newtonian behaviour the viscosity varies with shear rate, thus vis-
cosity may be deﬁned in many ways. The ‘apparent viscosity’ is the value of viscosity
evaluated at some nominal average value of the shear rate (Hackley and Ferraris, 2001).
When using an equipment with control of shear rate the term ‘coeﬃcient of viscosity’
(or the abbreviated form ‘viscosity’) should be used to designate the ratio of shear stress
to shear rate under simple steady shear (Hackley and Ferraris, 2001). The ‘diﬀerential
viscosity’ can be considered as the slope of the shear stress-shear rate curve. The ‘plastic
viscosity’, present in the Bingham model (see 2.2.3), corresponds to the diﬀerential vis-
cosity determined in the linear portion of the ﬂow curve (high shear rates zone) (Hackley
and Ferraris, 2001).
11
2. Rheology of cement suspensions
Figure 2.2.: Flow curve types (Saak, 2000)
Observing Figure 2.2, a material is said to be shear-thinning (or pseudoplastic) and
shear- thickening (or dilatant) when the diﬀerential viscosity respectively decreases and
increases with shear rate. The viscoplastic behaviour is characterized by the existence
of a yield stress (σ
0
), with the material behaving like a solid below the yield stress, but
ﬂowing like a viscous liquid when this stress is exceeded. This ﬂow curve may be linear
(Bingham behaviour) or non-linear (see Figure 2.2). The viscoplastic behaviour is found
in ﬂocculated suspensions, like cement paste (Saak, 2000; Hackley and Ferraris, 2001;
Struble, 2001). A gel structure forms immediately after water is added to cement, due
to a combination of colloidal interparticle forces and chemical reactions (Saak, 2000).
But, the ﬂocculated structure of fresh cement paste can be broken apart by applying
a suﬃciently high shear force (Saak, 2000). Some controversy exists about the real
existence of ‘yield stress’ as a material property (Wallevik, 2003b; Møller et al., 2006;
Esping, 2007). According to Wallevik (2003b), from the practical point of view, a yield
stress value exists for high concentration coarse particle suspensions like fresh mortar and
concrete, relative to the time and shear rate which is of interest for cement suspensions.
Time dependent ﬂuid behaviour
In practice, viscosities may depend not only on the rate of shear but also on the time for
which the ﬂuid has been subjected to shearing. Time-dependent ﬂuid behaviour may
be further sub-divided into two categories: thixotropic and anti-thixotropic (Chhabra
and Richardson, 1999).
The deﬁnition of thixotropy is a decrease of viscosity under shear stress, followed by
gradual recovery when stress is removed (Wallevik, 2003b). If the ﬂow curve is mea-
sured in a single experiment in which the shear rate is steadily increased at a constant
12
2.2. Rheology
Figure 2.3.: Variation of shear stress with shear rate for time dependent ﬂuid material
(Chhabra and Richardson, 1999)
rate from zero to some maximum value and then decreased at the same rate to zero
again, a hysteresis loop of the form shown in Figure 2.3 is obtained. The height, shape
and enclosed area of the hysteresis loop depend on the duration of shearing, the rate of
increase/decrease of shear rate and the past kinematic history of the sample. No hys-
teresis loop is observed for time-independent ﬂuids, that is, the enclosed area of the loop
is zero (Wallevik, 2003b). There are relatively few anti-thixotropic ﬂuids (Chhabra and
Richardson, 1999), that is, ﬂuids for which the viscosity increases with time of shearing.
Again, hysteresis eﬀects are observed in the ﬂow curve, but in this case, it is inverted,
as compared with a thixotropic material (see Figure 2.3).
Cementitious materials generally show a thixotropic behaviour (Wallevik, 2003b; Rous-
sel, 2005; Esping, 2007). This behaviour originates from the microstructure of the matrix
system, due to coagulation and ﬂocculation of suspended particles and the time taken
to change this microstructure. As the suspension is sheared, the weak physical bonds
among particles are ruptured, and the network among them breaks down into separate
agglomerates, which can disintegrate further into smaller ﬂocs whereas if the suspension
is at rest the particles will start to coagulate or ﬂocculate into agglomerates again.
Viscoelastic ﬂuid behaviour
Many materials show both elastic and viscous eﬀects under appropriate circumstances,
meaning that the shear stress depends both on the shear strain and shear rate. In
the absence of the time-dependent behaviour mentioned previously, these materials
are said to be viscoelastic (Chhabra and Richardson, 1999). Flocculated suspensions
show viscoelastic behaviour at low strains (below the yield stress) (Struble, 2001).
13
2. Rheology of cement suspensions
While static measuring techniques are appropriate to characterize both time-dependent
and -independent ﬂuid behaviours, dynamic measuring techniques (oscillatory tests)
or creep/recovery tests are necessary to characterize completely the viscoelastic ﬂuid
behaviour, as it will be further detailed in 2.3.3.
Complexity of cement paste ﬂow behaviour
As it was mentioned before the ﬂocculated state of cement particles (especially, for
colloidal particles, i.e. smaller than 1µm) is responsible for plastic behaviour, with the
yield stress reﬂecting the forces holding particles together. Often this breakdown is
not complete at the yield stress, so the suspension is still somewhat ﬂocculated even
though it ﬂows, and this remaining ﬂocculation is progressively disrupted as the shear
rate is increased (Struble, 2001; Wallevik, 2003b). This can explain the shear thinning
behaviour observed with cement paste, for a limited shear rate range (see Figure 2.4 (a)).
But, it can also show shear thickening behaviour at higher shear rates, as can be observed
in Figure 2.4 (b)). According to Wallevik (2003b) the upper limit of the shear rate range
which is of interest to cement paste is as high as 60 to 80 s
−1
.
Another important aspect of cement paste is the extent to which hysteresis is observed
between the stress of the up- and down curves in a shear ramp test. As can be observed
in Figure 2.5 the stress in the up-curve is higher than the stress in the down-curve,
for a given shear rate. This hysteresis reﬂects some lack of equilibrium between the
microstructure and the shear rate, often because the material is undergoing some type
of structural breakdown during shear (thixotropy). For this reason it is important to
achieve full structural breakdown at each shear rate when calculating material param-
eters from a ﬂow curve (for example, when adjusting the Bingham model). A way to
achieve a complete structural breakdown during testing at each shear rate is to use the
stepped shear test instead of a ramped test and to use only the down-curve data. A
stepped shear test allows a waiting period for the equilibrium condition to be reached
while the ramp shear test does not (see Figure 2.6). Furthermore, the time required
to obtain equilibrium at each shear rate is shorter during the down part of the test
because one is going from higher degree of dispersion to a lower one (Wallevik, 2003b).
At low shear rates a suspension may be very slow to reach equilibrium. In the case of
cement paste, it may not be possible to wait for such a long time, because of other time-
dependent processes that are going on, like cement hydration. To be able to compare
measurements in common references and minimize the inﬂuence of past shear history
(due to mixing and handling of sample) it is advisable to pre-condition the sample before
the start of the viscometry test (Saak, 2000; Esping, 2007).
Besides the reversible time-dependent behaviour (thixotropy), cementitious materials
also show a time-dependent irreversible behaviour due to concurrent processes like the
14
2.2. Rheology
(a) (b)
Figure 2.4.: Shear-thinning and shear thickening behaviours of cement paste depending
on the shear rate range(CEM I 42,5 R; limestone ﬁller (Micro100); super-
plasticizer (Viscocrete 3000); w/c=0,35; wf/wc=0,39; Sp/p=1%; 25
◦
C)
(a) (b)
Figure 2.5.: Flow curves for cement paste showing hysteresis when shear rate is increased
and then decreased (CEM I 42,5 R; limestone ﬁller (Micro100); superplas-
ticizer (Viscocrete 3000) w/c=0,35; wf/wc=0,39; Sp/p=1%; 25
◦
C)
15
2. Rheology of cement suspensions
Figure 2.6.: Shear stepped and shear ramp modes in viscometry tests
growth of hydration products and water consumption during cement hydration, the
loss of water by evaporation and the loss of dispersing eﬃciency of the superplasticizer
(or other water reducing admixtures) (Esping, 2007; Struble, 2001). The inﬂuence of
hydration progress on the ﬂow behaviour of cement pastes is illustrated in Figure 2.7.
In this ﬁgure the evolution of ﬂow curves of cement paste samples stored in a closed
recipient at room temperature (about 25
◦
C) and cement paste samples stored in a closed
recipient at approximately 0
◦
C is compared. The rheology of cement paste samples
stored at 0
◦
C show much less dependence on time than cement pastes stored at 25
◦
C.
It is well-known that at 0
◦
C temperature the hydration reactions stop, in practice. Since
chemical reaction kinetic’s have an exponential dependence on temperature, decreasing
temperature slows down the pastes rheology evolution . It should be noted that, in this
case, the observed increase of shear stress (and viscosity), for a given shear rate, with
time is irreversible; and it should not be confused with thixotropy. Thixotropy eﬀects
were mitigated by appropriate pre-conditioning of samples and a shear stepped test.
2.2.3. Rheological models
Many mathematical expressions of varying complexity and form have been proposed
in the literature to model non-Newtonian behaviour (Ferraris, 1999b; Hackley and Fer-
raris, 2001). Only a selection of the more widely used viscosity models for cementitious
materials is presented here. To characterize the time-independent ﬂow behaviours the
Power-law, Bingham and Hershel-Buckley models can be used. These models are well
suited for studying materials over a small shear rate range, under equilibrium steady
ﬂow conditions. To describe the time-dependent ﬂuid behaviour (transient ﬂow) of a
16
2.2. Rheology
(a) (b)
Figure 2.7.: Inﬂuence of cement hydration reactions on the evolution of ﬂow curves of
cement paste: (a) without conservation, at about 25
◦
C, and (b) with con-
servation, at about 0
◦
C
suspension the “Coagulation Rate Theory”, developed by Hattori and Izumi, can be
used (Wallevik, 2003b), or a simpler model like the one proposed by Coussot et al.
(2002).
Power-law (or Ostwald-de Waele)
The Power-law allows describing the general non-Newtonian behaviour in materials that
show a negligible yield stress and a varying diﬀerential viscosity. The relationship be-
tween shear stress and shear rate is of the form
σ = k
˙
γ
n
(2.5)
where k and n are two curve-ﬁtting parameters. For shear-thinning ﬂuids, n assumes
a value between zero and one; while for shear-thickening n will be greater than one.
When n equals one the equation (2.5) reduces to equation (2.1), which describes the
Newtonian behaviour. The value of k can be viewed as the value of viscosity at unity
shear rate.
Bingham
The Bingham relation describes the behaviour of viscoplastic ﬂuids exhibiting yield
stress. The ideal Bingham material is an elastic solid at low shear stress values and a
17
2. Rheology of cement suspensions
Newtonian ﬂuid above a critical value called the Bingham yield stress. The relationship
between shear stress and shear rate is of the form
σ = σ
0
+ η
pl
˙ γ; σ ≥ σ
0
(2.6)
˙ γ = 0; σ < σ
0
(2.7)
where σ
0
and η
pl
are two curve ﬁtting parameters, that can respectively be interpreted
as yield stress and plastic viscosity. The Bingham model can describe the viscosity
characteristics of a ﬂuid with yield stress where viscosity is independent of shear rate,
as shown in Figure 2.2.
Herschel-Bulckley
The Herschel-Bulkley model can be seen as a combination of the Bingham and Power-
law models, to describe viscoplastic materials exhibiting a yield stress and a non-linear
ﬂow curve above the yield stress. The relationship between shear stress and shear rate
is of the form
σ = σ
0
+ η
pl
˙
γ
n
; σ ≥ σ
0
(2.8)
˙ γ = 0; σ < σ
0
(2.9)
where σ
0
, η
pl
and n are the three curve ﬁtting parameters. With the use of the third
parameter, this model provides a somewhat better ﬁt to some experimental data.
Hattori-Izumi
Hattori and Izumi developed the “Coagulation Rate Theory” which can be used as a
mathematical tool to describe thixotropy (Wallevik, 2003b). According to this theory,
the viscosity of a suspension as a function of time is given by
η = B
3
J
2/3
t
= B
3
¸
n
3
U
0
( ˙ γHt
2
+ 1) + Ht
(Ht + 1) ( ˙ γt + 1)
¸
2/3
(2.10)
where B
3
is the friction coeﬃcient between particles; n
3
is the number of particles; U
0
18
2.3. Rheological characterization
is the initial degree of dispersion and H is the coagulation rate constant (Wallevik,
2003b). The understanding of equation (2.10) and the concepts behind this theory is
not straightforward; a further explanation is given in (Wallevik, 2003b). This model
was applied and further developed in the numerical simulations carried out by Wallevik
(2003a).
Coussot
Coussot et al. (2002) suggested a simple model where viscosity is an increasing function
of λ, a parameter related to ﬂocculation level inside the material, and is given by
η = η
0
(1 + λ
n
) (2.11)
where η
0
is viscosity at inﬁnite shear rate when λ tends toward zero and n is a constant
positive parameter. An evolution equation is added
dλ
dt
=
1
θ
−α˙ γλ (2.12)
where
1
θ
is the ﬂocculation term and the second term in equation (2.12) can be associ-
ated with the deﬂocculation rate (Coussot et al., 2002). This model has been used by
Roussel to analyse rheological measurements obtained on cement pastes (Roussel, 2005).
2.3. Rheological characterization
2.3.1. Measurement instruments
There are essentially two methods for the rheometric measurements of ﬂuids: the cap-
illary and the rotational methods (see Figure 2.8 ). In capillary methods, the test ﬂuid
is made to ﬂow through a narrow tube because of hydrostatic or applied pressure. In
rotational methods the test ﬂuid is continuously sheared between two surfaces, one or
both of which are rotating (Hackley and Ferraris, 2001). Measurement instruments can
be one of two types: a viscometer or a rheometer (see Figure 2.8). A viscometer is
an instrument that principally allows measuring viscosity (or more precisely apparent
viscosity); it is simpler in design and less expensive than rheometers. In opposition, a
rheometer is an instrument used for the measurement of rheological properties over a
varied and extended range of conditions. A rotational rheometer allows a sample to be
19
2. Rheology of cement suspensions
Measuring methods
Capillary Rotational
Viscometer Viscometer Rheometer
Stress-controlled
Rate-controlled
Figure 2.8.: Measurement methods and instruments (Hackley and Ferraris, 2001)
sheared for a deﬁned period, under controlled shear stress (or shear rate) and under con-
trolled temperature conditions. Rotational rheometers can also incorporate oscillatory
and normal stress tests to characterize viscoelastic properties of samples (Hackley and
Ferraris, 2001). For these reasons, rotational rheometers are better suited to characterize
the complex non-Newtonian behaviour of cement pastes.
2.3.2. Rotational rheometers
Rotational rheometers are high-precision instruments that can be sub-divided into: shear
rate-controlled and shear-stress controlled instruments. Some instruments have the ca-
pability of operating in either stress-controlled or shear-rate controlled modes. Instru-
ments producing oscillatory strains are available, and a few commercial systems permit
measurement of the normal stress. The rotational rheometer CVO-100, from Bohlin
Instruments, used in the current work has all these facilities, except the normal stress
measurement. The main parts of this type of instrument are presented in Figure 2.9.
In stress-controlled measurements, a constant torque is applied to the measuring tool
in order to generate rotation, and the resulting rotation speed is then determined. The
rotation speed is then converted into a corresponding shear rate, based on the geome-
try of the measuring tool. In rate-controlled measurements, a constant rotation speed
is maintained and the resulting torque generated by the sample is determined using
a suitable stress-sensing device, such as a torsion spring or strain gauge (Hackley and
Ferraris, 2001).
20
2.3. Rheological characterization
(a)
(b)
(c)
(d)
Figure 2.9.: Main parts in the rotational rheometer used in the present work (CVO-
100, from Bohlin Instruments): (a) gap size indicator; (b) device to apply
a constant torque (or rotation speed) to the tool and a device to determine
the shear rate (or stress) response, respectively; (c) measurement tool; (d)
thermostatic bath with temperature control
Measuring tools
In the rotational rheometers the ﬂuid can be sheared between rotating cylinders, cone
and plate or parallel plates, as shown in Figure 2.10. The main advantages and dis-
advantages associated with these measuring tools are summarized in Table 2.2. For
non-Newtonian ﬂuids, even a simple determination of a shear rate versus shear stress
is far from being straightforward. Direct measurements of shear rate can only be de-
termined directly if it is constant (or nearly so) through the measuring tool employed.
Very narrow shearing gap measuring tools provide good approximations to this require-
ment. But, a shearing gap size is required to ensure adequate bulk measurements (a gap
size approximately 10 to 100 times the size of the largest ‘particle’ size (Chhabra and
Richardson, 1999); the gap size must be at least 10 times larger than the mean particles
size (Bohlin Instruments Ltd, 1994)), and this may conﬂict with the gap size required to
ensure near constant shear rate, within the gap. This restriction is more important for
both concentric cylinders and cone and plate tools, since in the case of parallel plates,
the gap size can be adjusted.
For cementitious materials, the concentric cylinders is the most commonly used mea-
suring tool (Saak, 2000; Esping, 2007). Several conﬁgurations have been developed:
‘cone’ and ‘hollow cavity’ to overcome the end-eﬀects due to shear ﬂow at the bot-
tom of the cylinder; ‘double-gap’ for low viscosity ﬂuids (see Figure 2.11); ‘vane’ to
21
2. Rheology of cement suspensions
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22
2.3. Rheological characterization
(a) (b) (c)
Figure 2.10.: Measuring tools available for rotational rheometers: (a) concentric cylin-
der; (b) cone and plate; (c) parallel plate (Hackley and Ferraris, 2001)
eliminate slippage
1
(see Figure 2.12) (Saak, 2000; Hackley and Ferraris, 2001; Esping,
2007). In addition, to prevent slippage the surface of the cylinder can also be sawn or
otherwise roughened (Bohlin Instruments Ltd., 2004). A well-known commercial ap-
paratus using this system is the BML rheometer for concrete (Wallevik, 2003b). The
cone and plate tool is another widely used measuring tool. The small cone angle (gen-
erally, ≤ 4
◦
) ensures that the shear rate is constant throughout the shearing gap, this
being of particular advantage when investigating time-dependent and non-Newtonian
materials, because the entire sample experiences the same shear history (Chhabra and
Richardson, 1999). In contrast to the cone and plate tool, with the parallel plates the
shear rate varies radially and the gap height may be varied (Chhabra and Richardson,
1999). The large gap sizes available can be used to overcome the limitations encoun-
tered using the cone and plate, such as gap size as compared to maximum particle
size. A well-known commercial apparatus using the rotating parallel-plates system is
the BTRHEOM rheometer for concrete (Ferraris, 1999b).
Temperature control and moisture loss prevention
Temperature control is extremely important when making rheological measurements.
In the CVO-100 rheometer (Bohlin Instruments) the temperature control units are
mounted in a clamp on the base of the instrument as illustrated in Figure 2.9 (d);
it provides temperature control by circulating water from a bath. When dealing with
high concentration samples of low volume, even low moisture loss can also have a critical
eﬀect on measured rheological properties. To minimise moisture loss during rheological
tests a vapour hood incorporating a solvent trap (ﬁlled with water) was employed (see
Figure 2.13) (Bohlin Instruments Ltd., 2004).
1
Formation of a water-rich layer near the smooth walls.
23
2. Rheology of cement suspensions
(a) (b) (c)
Figure 2.11.: Diﬀerent conﬁgurations for concentric cylinders: (a) double gap; (b) cone
and plate at the bottom; (c) hollow cavity at the bottom to trap air (Hack-
ley and Ferraris, 2001)
(a) (b) (c)
Figure 2.12.: Diﬀerent conﬁgurations for vane geometries used in concrete rheometers:
(a) two-point test (Tattersall); (b) IBB; (c) BML (Hackley and Ferraris,
2001)
Figure 2.13.: Vapour hood incorporating a solvent trap
24
2.3. Rheological characterization
Testing techniques
Small deformation
amplitude
Large deformation
amplitude
Creep/recovery test Viscometry test Dynamic oscillatory
shear test
Shear stepped test
Shear ramp test
Figure 2.14.: Rheological testing techniques (Bohlin Instruments Ltd, 1994)
2.3.3. Testing techniques
Rheological testing techniques available in a rotational rheometer can be divided in two
groups depending on the amplitude of shear strain rates applied during the test, namely,
small and large amplitude deformation tests (see Figure 2.14). Both viscometry tests
(shear stepped and shear ramp) can be used to characterize the ﬂow behaviour of ﬂuid
materials, or to obtain ﬂow curves like those presented in Figure 2.2. In a stepped
shear test individual shear values are selected and each shear is applied for a given time,
deﬁned by the user; shear rate, shear stress and viscosity are recorded for each shear
level. In opposition, in a ramp shear test a continuously increasing or decreasing shear
is applied and measurements are taken at time intervals deﬁned by the user. If one is
interested in measuring the progressing changes in cement paste due to hydration, a
viscometric test is not adequate because the shear rates necessary to cause ﬂow are ‘too
high’, thus breaking the fragile hydrated microstructure. In this case, creep/recovery or
low-amplitude oscillatory tests can be used (Struble and Sun, 1995; Zhang, 2001). The
deformations involved in these tests are small and kept within the linear viscoelastic
region, so the structural build-up can be monitored over time without destroying the
structure (Struble and Sun, 1995; Zhang, 2001).
In creep/recovery tests, a stress is applied for a time period (deﬁned by the user) and re-
sulting shear strain is measured. Since the change of strain will depend upon the applied
stress, results are usually presented as ‘creep compliance’ (J = shear strain/shear stess)
versus time. Recovery compliance is also measured when stress is removed. The
creep/recovery behaviour for an elastic solid is quite distinct from that observed for
a viscous ﬂuid (Struble et al., 1998). In oscillatory shear test an oscillating shear stress
(σ) (for example, a cosine wave) continuously excites the sample, thus the induced strain
25
2. Rheology of cement suspensions
Figure 2.15.: Oscillatory shear strain out-of-phase with stress by a phase angle of δ
response (γ) will also follow a cosine wave equation as follows
σ = σ
0
cos (wt) (2.13)
γ = γ
0
cos (wt −δ) (2.14)
where σ
0
is stress amplitude; γ
0
is strain amplitude; w is angular amplitude; t is time;
and δ is the phase angle (Hackley and Ferraris, 2001)(see Figure 2.15). For an ideal
solid, since strain is directly related to stress, it will be at a maximum when stress is
maximum and zero when stress is zero, thus the phase angle will be δ = 0
◦
. If the
material is purely viscous, it will be shear strain rate that is in phase with applied stress
and not strain, the phase angle will be δ = 90
◦
. In this type of test the material rheolog-
ical characteristics are described in terms of the shear storage modulus G

(representing
the elastic response of the material), and the shear loss modulus G

(representing the
irreversible viscous response) calculated as
G

=
σ
0
γ
0
cos δ (2.15)
G

=
σ
0
γ
0
sin δ (2.16)
There have been relatively few studies examining viscoelastic properties of cement
26
2.3. Rheological characterization
Figure 2.16.: Evolution of storage and loss modulus of a cement paste as a function of
hydration time (frequency=1Hz)
pastes due primarily to equipment limitations. The major challenge in applying small
amplitude oscillatory technique to the cement paste is how to get reliable data. The
storage modulus of paste can get as high as 10
6
Pa and the critical strain is very
low (10
−4
) (Zhang et al., 2001)(see Figure 2.16). Small shear strain results in low
sensitivity while a too large shear strain leads to a microstructural breakdown (Zhang
et al., 2001)(see Figure 2.17). Accurate measurements require a torsion bar that is
sensitive enough to measure such small strains while stiﬀ enough to overcome the high
modulus of the cement suspensions.
Most rheological testing on cement paste is to measure viscosity, yield stress and thixotropy.
To accomplish it, several methods are suggested in the literature, based on diﬀerent test-
ing techniques.
Viscosity
For non-Newtonian ﬂuids, like cement paste, a multipoint ﬂow curve has to be measured.
A single point result of viscosity does not describe this material completely. It is possible
that two cement pastes with completely diﬀerent rheological properties generate the
same value of viscosity at a given value of shear rate if the ﬂow curves intersect at
this point. As stated before, to reach equilibrium conditions at each measurement the
stepped shear test is preferred.
27
2. Rheology of cement suspensions
Figure 2.17.: Applied shear strain and phase angle as a function of hydration time
(frequency=1Hz)
Yield stress
The most direct method for yield stress measurement is provided by stress-controlled
rheometers; it involves applying a gradually increasing stress and monitoring the stress-
time proﬁle for an inﬂexion in the curve, i.e. the onset of ﬂow. The development of
viscoelastic eﬀects is represented by the departure from linearity of the shear stress-time
curve. From this curve, yield stress can be evaluated either from the point where the
elastic response starts to diverge from its linearity (called static yield stress) or at the
peak stress (called dynamic yield stress) (Saak, 2000) or the stress at the plateau beyond
the peak (called equilibrium yield stress) (see Figure 2.18) (Møller et al., 2006). Struble
et al. (1998) used multiple creep/recovery tests to determine changes in yield stress
during hydration. This method is probably the most accurate way of characterizing the
yield stress but it can be a very time consuming process (Møller et al., 2006).
Another possibility is an indirect determination of the yield stress, which involves the
extrapolation of experimental shear stress versus shear rate data, from a ﬂow curve,
to zero shear rate. Often this is performed by ﬁtting a suitable rheological model rep-
resenting the ﬂuid, where the yield stress corresponds to one of the model parameters
(see paragraph 2.2.3). The yield stress measurement is very sensitive to the experimental
procedure, the measuring tool, the occurrence of slippage and depends on the applied
shear rate (Møller et al., 2006; Saak, 2000). Although it is often considered as a single
ﬂuid parameter, the yield stress as a true property of a suspension is a controversial
issue (Wallevik, 2003b; Saak, 2000). Based on recent experimental ﬁndings, a model
was suggested to describe a interplay between yield stress and thixotropy (see equa-
tion (2.11) and (2.12)) (Coussot et al., 2002; Møller et al., 2006). Thixotropy and yield
28
2.3. Rheological characterization
Figure 2.18.: Schematic time evolution of the stress for imposed shear rate experiments
at diﬀerent imposed rates and diﬀerent deﬁnitions of yield stress, namely,
static, dynamic and equilibrium (Møller et al., 2006)
stress are believed to be caused by the same fundamental physics, but are traditionally
modelled as separate phenomena (Møller et al., 2006).
Thixotropy
The most common method used to quantify thixotropy is to determine the enclosed
area between the up- and down-curves, obtained with a shear-ramp test (Saak, 2000;
Wallevik, 2003b). In a shear-ramp test, shear stress (or shear rate) is ramped at a
ﬁxed speed up to a maximum value, then ramped back down at the same speed to the
beginning (often called hysteresis loop) (see Figure 2.5). The test result depends on the
shear history of the sample and on how rapidly the stress or shear rate was ramped.
Another possible approach consists in applying a constant shear stress (or rate) until a
equilibrium state is reached. The equilibrium shear stress value represents the minimum
stress that can be obtained at a given shear rate (see Figure 2.19 (a)). The diﬀerence
between the peak stress (σ
peak
) and equilibrium stress (σ
eq
) is a measure of thixotropy.
Using several samples at diﬀerent shear rates, the equilibrium approach can be used to
construct the peak as well as equilibrium ﬂow curves (see Figure 2.19 (b)). This method
is considered superior to the hysteresis loop approach but it has the disadvantage, for
cement pastes, of requiring a large number of samples and tests to obtain the ﬂow
curves (Saak, 2000). Saak (2000) suggested a combined approach between equilibrium
and hysteresis approaches to capture the general trend of peak an equilibrium stress
ﬂow curves while using only one test.
29
2. Rheology of cement suspensions
(a) (b)
Figure 2.19.: (a) Equilibrium stress measurement of cement paste, and (b) evolution of
peak and equilibrium ﬂow curves for cement paste
2.3.4. Measuring sequence setup
The measuring sequence setup of all rheological measurements refered to in Chapter
6 is presented in this section. Rheological characterization of cement pastes is carried
out focused only on ﬂow behaviour. Yield stress and plastic viscosity parameters were
determined from ﬂow curve data by using the Bingham model.
A rotational rheometer CVO-100, from Bohlin Instruments, was used. Data acquisition
and control were performed with a PC-coupled to CVO-100 rheometer (see Figure 2.20).
The measuring device consisted of a cone and plate geometry. The cone diameter was
40 mm with a cone angle of 4
◦
, providing a gap of 150µm. A viscometry test (shear
stepped), in shear rate control mode, was selected to obtain equilibrium ﬂow curves.
Before measurements a pre-conditioning procedure was applied. The temperature was
controlled and kept constant at 25 ± 0, 1
◦
C during all testing sequence. A pre-shear was
applied to homogenize the sample during 45 seconds at a shear rate of 200 s
−1
, followed
by a resting period of 100 seconds (with no shear applied). During the measurements
the rheometer was programmed to perform a 12-step logarithmic increase of shear rate
ranging from 0, 1 to 200 s
−1
and back again to complete a full cycle. Each measurement
consisted of a delay and integration times. The shear rate was applied for the delay time
(set equal to 10 s), but no information was recorded during this period. The average
value of shear stress was measured for the integration time (set equal to 5 s) and the
viscosity was then calculated as the ratio of average shear stress by imposed shear rate.
The complete testing sequence as a function of time, including the pre-conditioning and
30
2.3. Rheological characterization
Figure 2.20.: Rotational rheometer (Bohlin CVO-100) used in this study
Figure 2.21.: Testing sequence: ﬁrst, pre-shear; second, waiting period; and, third, mea-
suring period
measuring parts, is presented in Figure 2.21.
The descending part of the obtained ﬂow curves were ﬁtted to the Bingham model
(see equation (2.6)), as illustrated in Figure 2.22. Only data points corresponding to a
shear-thinning behaviour were used; in some cases the last one or two data points were
excluded because the material started to exhibit a shear thickening behaviour for these
higher shear rates. The cement paste used as example in Figure 2.22 can be characterized
by a yield stress of 1,218 Pa and a plastic viscosity of 0,545 Pa.s. The adjusted model
parameters (yield stress and plastic viscosity) were taken as the rheological test results
for the study presented in Chapter 6.
As can be observed in Figure 2.23, the area enclosed between the up- and down- curves
is almost zero, meaning that the adopted measuring sequence was eﬀective to eliminate
the time-dependent eﬀects, such as thixotropy. The selected measuring tool (cone and
31
2. Rheology of cement suspensions
(a) (b)
Figure 2.22.: (a) Up- and down-ﬂow curves, and (b) ﬁtted Bingham model to the down-
curve data
plate; 4
◦
/40 mm), which has the major advantage of imposing a constant shear rate
through the sample, may be subject to some criticism. The corresponding gap size
is of 150 µm allowing for a maximum ‘particle size’ of 15 µm, according to (Chhabra
and Richardson, 1999). As can be observed in tables C.1 to C.10, in Appendix C, the
coarser cements have particles of size as high as 90 µm. On the other hand, the mean
particle size of studied cement pastes was around 15 µm or lower since limestone ﬁller
(ﬁner than cement) was added to the mixtures, thus satisfying the requisite established
in (Bohlin Instruments Ltd, 1994). Furthermore, a superplasticizer was always included
preventing the formation of large ﬂocculates. Concerning the selected rheological model,
other authors suggested more complex models to characterize cement pastes behaviour
(Yahia and Khayat, 2001a). In the present study, the Bingham model was selected for
its simplicity (a low number of adjusting parameters) leading to rheological parameters
which are in good agreement with empirical test results (see Chapter 6).
2.4. Fresh concrete, mortar and cement paste as
particle suspensions
Fresh concrete, mortar and cement paste can be considered as diﬀerent types of sus-
pensions (Barnes et al., 1989; Struble, 2001; Walraven, 2003). A particle suspension
consists of two phases, namely, the suspended particles and the suspending medium.
Fresh concrete consists of particles with a broad range of sizes, shape, mass and surface
32
2.4. Fresh concrete, mortar and cement paste as particle suspensions
Figure 2.23.: Up- and down-ﬂow curves showing almost zero enclosed area
texture suspended in water. This applies also to mortar and cement paste.
2.4.1. Factors inﬂuencing the rheology of suspensions
The rheological behaviour of suspensions is controlled mainly by the volume fraction
of solid particles (concentration), the extent to which the particles are agglomerated or
ﬂocculated, particle shape characteristics and particle size distribution (Barnes et al.,
1989; Struble, 2001).
In general, three types of forces act on particles in a suspension: colloidal, Brownian and
viscous forces. Colloidal forces can cause a net attraction or repulsion between particles
due to such a factors as van der Waals forces and electrostatic charges. This type of
interactions between particles will be further discussed in Chapter 3. Brownian forces
cause a rapid and random motion in very small particles (smaller than 1 µm) (Barnes
et al., 1989; Struble, 2001). Brownian motion prevents settling of small particles even
though they are more dense than the suspending ﬂuid. The viscous forces are propor-
tional to the velocity diﬀerence between particles and the surrounding medium. Thus,
the suspension viscosity depends on the viscosity of the suspending medium (Barnes
et al., 1989). When a suspension is at rest (or under very low shear rates) Brownian or
colloidal forces dominate, for a given solids volume concentration below a critical value
(φ
c
) (Koehler and Fowler, 2007). A critical solids volume concentration is obtained when
a network of contacting particles exists and friction forces become dominant. Maximum
solids volume concentration (φ
m
) is deﬁned as the solids volume concentration at which
particle interference makes ﬂow impossible and the viscosity approaches inﬁnity (see
Figure 2.24). Suspensions with low solids volume fractions and low shear rates exhibit
33
2. Rheology of cement suspensions
Figure 2.24.: Conceptual framework for rheology of concentrated suspensions (Koehler
and Fowler, 2007)
shear-thinning behaviour and are dominated by Brownian motion. As the concentra-
tion is increased, colloidal interactions dominate at low shear rates and the suspension
exhibits viscoelasticity, thixotropy, and yield stress (see Figure 2.24). If shear rate is
increased from friction zone, the imposed velocity gradient imposes an orientation of
the particle structure, a thin layer of ﬂuid exists between particles which lubricates the
contacts, then the viscosity is lowered (shear-thinning behaviour).
Colloidal particles forces dominate to a large extent the complex behaviour and time-
dependent behaviour of cement paste. Some authors argue that rheology of fresh con-
crete is much simpler than for dilute cement paste, due to the large volume fraction
of coarser particles (Wallevik, 2003b). For most sizes of aggregates, only viscous forces
are relevant. Thus the following interpretation of the Bingham model parameters (see
equation (2.6)) for mortar/concrete can be applied, as suggested by Ferraris and Larrard
(1998) (see Figure 2.25):
• the term σ
0
is a contribution of the aggregate skeleton (results of friction between
the particles) and is an increasing function of φ/φ
m
and
• the term η
pl
˙ γ is a contribution of the suspending medium; where η
pl
depends on
the viscosity of the suspending ﬂuid and is an increasing function of φ/φ
m
.
Thus, the solid particles concentration relative to the maximum solid concentration
(φ/φ
m
) is a determining parameter for the rheology of mortar/concrete suspensions
(de Larrard, 1999). It determines the number and nature of the contacts between par-
ticles just before initiating ﬂow and the average distance between particles, during ﬂow.
34
2.4. Fresh concrete, mortar and cement paste as particle suspensions
Figure 2.25.: Physical interpretation of the Bingham model (de Larrard, 1999)
An excess of ﬂuid, beyond the minimum necessary to ﬁll the pores, is necessary to re-
duce the friction between the particles. Thus, typical solid particles concentration in
cement suspensions is signiﬁcantly lower than φ
m
. Besides, solid particles concentration
is higher in concrete than in cement paste, is around 0,8 in the case of ordinary con-
cretes, whereas, in the case of SCC, is around 0,6 (Roussel, 2006a). As a reference, the
maximum packing fraction of monosized spheres is 0,74 in hexagonal structure and 0,52
in a cube structure. Mortar with water/cement/sand ratio 0,5/1/3 has φ
m
∼ 0, 75 and
high strength concrete can go up to 0,85 or higher (Wallevik, 2003b). Another impor-
tant diﬀerence between cement paste and concrete is the range of strain rates. During
placing, concrete is sheared at only about 1 to 10 s
−1
; while cement paste (in concrete)
experiences signiﬁcantly higher shear rates (60 to 80 s
−1
), because the cement paste is
’squeezed’ between the aggregates (Wallevik, 2003b; Saak, 2000; Ferraris, 1999a). The
shear rate applied to cement paste is around ﬁve times higher in the case of ordinary
concrete and around two to three times higher in the case of SCC.
Various models can be found in the literature for the viscosity of suspensions. For low
solids volume concentration (φ ≤ 0, 01) suspensions of monosized spheres, Einstein sug-
gested the following relationship
η = η
s
(1 + 2, 5φ) (2.17)
where η is the viscosity of the suspension and η
s
is the viscosity of the suspending
medium (Barnes et al., 1989). This equation clearly shows that particles increase vis-
cosity of a suspending medium as a function of their concentration; but the eﬀects of
particle size and interaction between particles are not taken into account (Barnes et al.,
35
2. Rheology of cement suspensions
1989). For concentrated suspensions Krieger and Dougherty developed the following
relationship
η = η
s

1 −
φ
φ
m

−[η]φ
m
(2.18)
where φ
m
is the maximum solids volume concentration and [η] is called the intrinsic
viscosity deﬁned as
[η] = lim
φ→0

η
η
s
−1

φ
(2.19)
In Krieger and Dougherty relationship, the apparent viscosity is expressed as a func-
tion of solids volume concentration with additional parameters, namely, φ
m
and [η]. [η]
accounts for particle shape characteristics while φ
m
accounts for particle shape charac-
teristics, degree of ﬂocculation, and particle size distribution (Struble and Sun, 1995).
A material with higher maximum solids fraction, due to favourable particle shape char-
acteristics, particle size distribution, and lack of ﬂocculation, results in lower relative
viscosity at a given solids volume fraction (Barnes et al., 1989). The maximum solid
fraction and intrinsic viscosity vary with shear stress and shear rate (Barnes et al., 1989;
Struble and Sun, 1995). The Krieger-Dougherty equation has been applied successfully
to cement paste (Struble and Sun, 1995; Vikan, 2005) and mortar (Toutou et al., 2005),
but was not so adequate to model concrete (Toutou et al., 2005). For cement paste
dispersed with superplasticizer, Struble and Sun (1995) estimated the intrinsic viscos-
ity to be approximately 5 and maximum solids volume fraction to be approximately
0,7. Vikan estimates of maximum volume fractions of solids ranged from 0,5 to 0,6 and
intrinsic viscosity at about 5 (Vikan, 2005). De Larrard (1999) found that plastic viscos-
ity of mortar and concrete increases exponentially with normalized solid concentration
(φ/φ
m
).
2.5. Concrete rheometeres
Unlike mortar and cement paste, concrete requires specially designed rheometers. De-
signing a rheometer for testing fresh concrete presents considerable challenges. The
suspension contains particles with a wide range of sizes (from less than 1 µm to 30 mm)
(Esping, 2007). This wide range results from the heterogeneous concrete composition,
which includes cement (5µm to 60µm), mineral ﬁllers (< 1 µm to 100 µm ), ﬁne ag-
gregates (0,1 mm to 4 mm) and coarse aggregates (4 mm to 30 mm or higher in some
special concretes). The larger particles tend to settle due to gravity and this segrega-
tion is aggravated during shear due to the thixotropic behaviour of cement paste. The
36
2.6. Rheology of SCC
occurrence of slippage is also a common problem with concrete rheometers.
The smallest gap in the instrument should be 3 to 5 times the largest diameter of
the coarse aggregate to obtain a representative sample and to avoid the interlocking
of the aggregates that will prevent ﬂow (Ferraris et al., 2001a). On the other hand,
with concentric cylinders, the gap between the cylinders needs to be relatively small as
compared to their diameters (the ratio of the radii of the two cylinders should be between
1 and 1,1) to provide a uniform shear rate (Ferraris et al., 2001a). As an example, a
concrete with an aggregate maximum size of 10 mm, would require a gap size of 50
mm and an inner and outer cylinders with a radius of 0,5 m and 0,55 m, respectively.
Such a ‘wide-gap’ rheometer was built by Coussot (CEMAGREF) and used for fresh
concrete to validate the results obtained with a ‘narrow-gap’ rheometer (BTRHEOM)
developed at the Laboratoire Central des Ponts et Chaussées (LCPC) (Ferraris et al.,
2001a). Obviously, this type of instrument is unsuitable for ﬁeld use because it would
not be easily transported outside the laboratory.
To overcome some of these limitations, various rheometers with measuring tools con-
sisting of a shaft with blades were developed after the “Two-point-test” workability
apparatus designed by Tattersall. The original “Two-point test” was further modiﬁed
and improved by Beaupré (IBB) and by Wallevik and Gjφrv (BML) (see Figure 2.12).
There is also a rheometer that uses the parallel plate measuring tool (BTRHEOM) that
was developed by de Larrard et al. at LCPC. Further descriptions of these tests can be
found elsewhere (Banﬁll et al., 2000).
2.6. Rheology of SCC
2.6.1. Target area (Bingham parameters)
As long as steady state ﬂow is reached, the rheological behaviour of fresh concrete
may be simply described using the Bingham or Herschel-Bulkley models (Roussel, 2005;
Wallevik, 2003b). A wide range of SCC mixtures can be obtained. Wallevik (2003b)
evaluated various SCC mixtures, from diﬀerent countries, with the BML-rheometer,
resulting in the area presented in Figure 2.26. SCCs exhibited almost no yield value, 0
to 60 Pa, and a wide range of plastic viscosity, 20 to more than 120 Pa.s (very variable
between countries) (Wallevik, 2003b). The recommended combination of yield stress
and plastic viscosity values for SCC by IBRI (ACM Centre, 2005) are represented by
the inner area in Figure 2.26. The minimum slump ﬂow values required to obtain
SCC mixes with diﬀerent plastic viscosities are also shown in Figure 2.26. The main
diﬀerence between SCC and conventional concrete is the yield stress. The change in
concrete yield stress is directly related to the paste rheology because of the dispersing
37
2. Rheology of cement suspensions
Figure 2.26.: Target area for SCC and corresponding slump ﬂow (ACM Centre, 2005)
eﬀect of superplasticizer, which acts on the paste level. The SCC plastic viscosity is not
reduced to near zero because of the need for a suﬃciently high paste viscosity to prevent
segregation (in dynamic conditions) and the contribution of aggregates to increase solid
particles concentration.
2.6.2. Thixotropy
Concrete contains thixotropic cement paste, therefore displays also a thixotropic be-
haviour (Roussel, 2005). Recent approaches to quantify thixotropic behaviour of SCC
have focused on the ﬂocculation at rest, because this has practical consequences on
formwork pressure, multi-layer casting and stability of SCC. Over short timescales the
reversible time-dependent eﬀects (thixotropy) dominate while over larger time scales
it is the hydration (irreversible) eﬀects that dominate (Jarny et al., 2005). Accord-
ing to Roussel a Bingham model is suﬃcient to describe the steady state ﬂow of fresh
concrete and the yield stress at rest increases linearly as a function of time (Roussel,
2006a)(Roussel 2006), according to the following model
σ = (1 + λ) σ
0
+ η
pl
˙ γ (2.20)
where the model parameters have the same meaning as presented in equations (2.6)
and (2.11). The parameter λ evolves from an initial value of zero (just after mixing,
maximum shearing phase) to a positive value according to equation (2.12). At rest (zero
shear rate), the evolution of yield stress is given by
σ
0
(t) = (1 + λ) σ
0
= σ
0
+ A
thix
t (2.21)
38
2.6. Rheology of SCC
Sheared and resting zones and their respective dimensions when
Figure 2.27.: Sheared and resting zones when concrete is cast from the top (Ovarlez and
Roussel, 2006)
where A
thix
is the ﬂocculation rate of concrete that is ﬁtted from experimental results;
this parameter is of most interest for practical application of SCC (Roussel, 2007).
Formwork pressure
In the literature, contradictory values of lateral stresses in the formwork when casting
with SCC can be found (Billberg, 2003). Initially, these diﬀerences were attributed to
diﬀerent casting rates, then it was concluded that the thixotropic behaviour of SCC plays
also an important role (Billberg, 2003). The lateral stress is equal to the hydrostatic
pressure when the casting rate is high or when the concrete is injected from the bottom
of the formwork because the material is not able to ﬂocculate and thus keeps on behaving
as a ﬂuid. The lateral stress does not reach the hydrostatic pressure when the material
is cast from the top of the formwork slowly enough to ﬂocculate and withstand the load
of concrete cast above it. The lateral stress decreases quickly in the part where concrete
is at rest (see Figure 2.27) because it builds up an internal structure (starts behaving as
a solid) and has the ability to withstand the load from concrete cast above it, without
increasing the lateral stress against the formwork (Ovarlez and Roussel, 2006). Ovarlez
and Roussel proposed a model that physically links the consequences of thixotropy and
the evolution of the lateral stress during and after casting; for further details on this
model see (Ovarlez and Roussel, 2006).
Multi-layer casting
During placing, a layer of SCC has a short time to rest and ﬂocculate before a second
layer of concrete is cast above it. If it ﬂocculates too much and its yield stress increases
39
2. Rheology of cement suspensions
above a critical value, then the two layers do not mix at all and, as vibrating is prohibited
in the case of SCC, this creates a weak interface in the ﬁnal structure. This can result
in signiﬁcant strength loss of hardened concrete (Roussel, 2007).
Stability of SCC
Due to density diﬀerences between the various concrete constituent materials, hetero-
geneities induced by gravity may occur in concrete, namely, bleeding and segregation
(Roussel, 2006a). Bleeding concerns the water migration to the concrete surface, while
segregation concerns the movement of coarser particles (upward or downward, depending
on the density of particles relative to the density of the suspending medium). Segre-
gation may occur in both static and dynamic conditions. In dynamic conditions other
factors than gravity (the only present in static conditions) may induce segregation like
the existence of obstacles or conﬁned sections (Roussel, 2006a). The static segregation
tendency in a given mixture depends on the yield stress of the suspending Bigham ﬂuid
and not on its plastic viscosity sections (Roussel, 2006a; Saak, 2000). During placing,
the cement paste (inside the concrete) is deﬂocculated because of the high shear rates
applied during the mixing and of the casting itself. This allows high deformability of
concrete but, as soon as casting is over and before setting, gravity may induce sedi-
mentation of the coarsest particles resulting in large heterogeneities of the hardened
concrete, as it was observed during full-scale tests carried out during BACPOR research
project (Nunes et al., 2005b). A thixotropic cement paste will, however, re-ﬂocculate
once at rest. Its yield stress will increase and could be suﬃcient to prevent the particles
settling (Roussel, 2006a).
In conclusion, a thixotropic (high ﬂocculation rate) or non-thixotropic (low ﬂocculation
rate) SCC mixture can be of interest depending on the application. A non-thixotropic
SCC should be used in concrete slabs where the multi-layer casting problem is dominant
and the segregation risk is reduced. In opposition, a thixotropic SCC should be used in
the case of walls in order to minimize the lateral stresses in the formwork and segregation
(Roussel, 2007). The substitution of cement with ﬁner powders (higher surface area)
such as silica fume or ﬂy ash was found to increase ﬂocculation rate (Roussel, 2007).
2.6.3. Computational model of concrete ﬂow
To beneﬁt from the full potential of SCC, numerical design of SCC mixtures with op-
timized performance relative to the geometry of the formwork, casting technique and
conﬁguration of reinforcement is needed (Gram et al., 2007; Roussel et al., 2007). Thus,
recent studies are focusing on the development of computational tools to model fresh
40
2.6. Rheology of SCC
concrete ﬂow, based on its rheological parameters. Various types of software and simu-
lation methods have been used:
• single ﬂuid simulations (e.g. Computational Fluid Dynamics (Wallevik, 2003a;
Roussel and Coussot, 2005; Thrane et al., 2005; Modigell et al., 2007; Waarde
et al., 2007)
• numerical modelling of discrete particle ﬂow (e.g. Distinct Element Method (Gram
et al., 2007) and Dissipative Particle Dynamics (Martys and Ferraris, 2002) and
• numerical techniques allowing the modelling of particles suspended in a ﬂuid
(Roussel et al., 2007).
A general description of each of these computational techniques along with their ad-
vantages/disadvantages for the modelling of the ﬂow of fresh concrete can be found in
(Roussel et al., 2007).
41
2. Rheology of cement suspensions
42
3. Cement-superplasticizer interactions
3.1. Introduction
This chapter presents an overview of relevant literature on parameters aﬀecting the
cement-superplasticizer interactions.
Dispersion of cement particles is recognized as the main way by which superplasticizers
improve the workability of concrete without increasing the water content (Björnström
and Chandra, 2003; Flatt, 1999; Griesser, 2002). However, dispersion eﬀect varies with
type of cement, the production plant (for the same cement type) and type of superplasti-
cizer (Flatt, 1999; Griesser, 2002; Hanehara and Yamada, 1999), as shown in Figure 3.1.
Quantifying this eﬀect is a diﬃcult task and is further complicated by the ongoing hydra-
tion reactions of cement. Firstly, an overview of the basic chemical aspects of cement
hydration is presented, after which the various physico-chemical concepts involved in
cement-superplasticizer interactions are then reviewed. Finally, the inﬂuence of molec-
ular structure parameters on the performance of polycarboxylate type superplasticizers
is discussed more in detail, since this type of superplasticizer was used in the present
work.
The present chapter provides theoretical background to discuss results presented in
Chapters 5 and 6.
3.2. Portland cement hydration
Cement grains microcospically show a mosaic surface resulting from the diﬀerent clinker
phases, namely, C
3
S, C
2
S, C
3
A, C
4
AF (see Table 3.1) and gypsum. The ﬁrst four min-
erals are formed during equilibrium conditions in the burning of cement clinker, while
gypsum is added to the mill when clinker is ground to cement. The distribution of sili-
cate and aluminate phases on the cement grain is determined by the milling process and
the diﬀerence in resistance against fracture. Cement hydration starts as soon as water is
added to the mix. The hydration of cement involves exothermic reactions, which may be
measured by the heat generated as a function of time, using an isothermal calorimeter.
43
3. Cement-superplasticizer interactions
Figure 3.1.: Fluidity variation with superplasticizer type and producing plants (Hane-
hara and Yamada, 1999)
Table 3.1.: Main phases of Portland cement and their characteristics (Griesser, 2002;
Jolicoeur and Simard, 1998; Moir, 2003)
Parameter C
3
S C
2
S C
3
A C
4
AF
Chemical formula Ca
3
SiO
5
Ca
2
SiO
4
Ca
3
Al
2
O
6
Ca
4
Al
2
Fe
2
O
10
Technical name alite belite aluminate phase ferrite phase
Typical range in clinker (%) 45 - 65 10 - 30 5 - 12 6 - 12
Reactivity high low very high low
Heat of hydration (J/g) 500 250 1340 420
Hydration product C-S-H; CH C-S-H; CH ettringite; monosulfate C
6
AFH
12
44
3.2. Portland cement hydration
Figure 3.2.: Heat of hydration of plain cement pastes incorporating diﬀerent cement
types determined by conduction calorimetry at 20
◦
C (Silva, 2007)
The curves obtained for diﬀerent cement types, supplied by Cimpor-Alhandra produc-
tion center, used in the present work are presented in Figure 3.2. Four stages can be
distinguished: an initial hydration period, a dormant period, acceleration and a decel-
eration period. With respect to fresh concrete properties (self-compactability) only the
initial hydration and the dormant period are important (Griesser, 2002; Szecsy, 2005).
3.2.1. Initial hydration (0-15 min)
Water added to the mix absorbs into the outer part of the cement grain, dissolving
easily soluble components like alkalis, calcium sulfate phases and free lime, which move
out into the surrounding water (Griesser, 2002; Moir, 2003). Na
+
, K
+
, Ca
2+
, SO
2−
4
and OH
−
ions enrich the pore water. An adequate supply of soluble calcium sulfate
controls the C
3
A hydration, thus preventing ﬂash set. Ettringite is formed around the
C
3
A containing surfaces. However, if SO
2−
4
concentration is too high, massive nucleation
and growth of gypsum crystals may occur (false set). Contrary to ﬂash set, false set
is reversible, because the gypsum needles, which have developed a structure in the
paste, can be broken and dispersed by re-mixing (see Figure 3.3). Thus, the cement
manufacturer needs to optimize the level of readily soluble sulfate in the cement and
match this to the reactivity of C
3
A. As it will be explained later, this is the most
important reaction for ﬂuidity of cement paste. Considering the relative reactivities of
aluminates and silicates, the initial gel products consist largely of aluminates though
an amorphous calcium silicate hydrate known as C-S-H gel forms very rapidly on C
3
S.
C
3
S reacts rapidly with water and the reaction is highly exothermic. The reaction rate
45
3. Cement-superplasticizer interactions
Figure 3.3.: Normal and false setting (Hanehara and Yamada, 1999)
of C
3
S is higher than that of C
2
S (Moir, 2003). Secondary gypsum may precipitate
from the supersaturated pore water. After some minutes, the cement grains are coated
with a protective layer of hydration products. At this stage the reactions appears to be
suspended and the heat ﬂux drastically decreases.
3.2.2. Dormant period (15 min-4 h)
The dormant period usually lasts several hours. This is of practical signiﬁcance because
it allows concrete to be placed. This period is characterized by a very low heat ﬂow.
Nevertheless, the surface gel layer (C-S-H phases) on the cement grains is thickening
and the ettringite needles are slightly growing (Jolicoeur and Simard, 1998).
3.2.3. Acceleration period (4-8 h)
Near the end of the induction period, the rate of cement hydration reactions sharply
increases. Several eﬀects have been considered to explain the start of the acceleration
period: disruption of the protective hydrates layer, nucleation and growth of C-S-H
phases or portlandite, recrystallization of ettringite (Jolicoeur and Simard, 1998). Dur-
ing the acceleration period the suspension loses its plasticity and is converted into a stiﬀ
matrix, which is no longer castable. Cement paste setting is arbitrarily deﬁned as the
time when a pat of cement paste oﬀers a certain resistance to penetration by a standard
probe. The fast rise of temperature is controlled mainly by the intense hydration of
C
3
S associated with the formation of C-S-H phases and the precipitation of portlandite.
C
3
A and to a lesser extent C
4
AF continue to hydrate. During the acceleration period
46
3.3. Physical interactions
the calcium and sulfate ion concentration in the pore water are decreasing due to the
ettringite formation.
3.2.4. Deceleration period (8-24 h)
This stage is characterized by hardening of the cement paste or concrete. In some ce-
ments, but not all, a small peak may be observed at the decreasing part of the curve
(see CEM II/A-L 42,5 R curve in Figure 3.2). This seems to be associated with re-
newed ettringite formation (Moir, 2003). Due to lack of sulfate ions in the pore water,
ettringite reacts with C
3
A to form a phase with a lower content SO
3
content known as
monosulfate (Moir, 2003). During the deceleration period the hydration reactions get
more and more diﬀusion controlled. Pore volume decreases with increasing time and
decreasing w/c ratio. Having completely hydrated, the cement mainly consists of C-S-H
gel and portlandite (Moir, 2003). As can be observed in Figure 3.2, total heat of cement
hydration (during the ﬁrst 48 hours) is highest for ﬁner cements with a high C
3
S and
C
3
A contents. The extent of hydration is strongly inﬂuenced by cement ﬁneness and
the proportion of coarse particles. Cement grains which are coarser than approximately
30 µm will probably never fully hydrate (Moir, 2003).
3.3. Physical interactions
3.3.1. Derjaguin–Landau Verway–Overbeek (DLVO) theory
Most colloid suspensions (particles smaller than 10µm) consist of particles with charged
surfaces (Yang et al., 1997). Depending on the charge of the particles, a cementitious
suspension can be in a dispersed or in a ﬂocculated state. Particle charges can be
either intrinsic or can result from interactions between two phases through dissolution,
adsorption, or ionization of interfacial surface groups. The electrostatic ﬁeld that arises
from these charges is described by the double layer model (Yang et al., 1997; Uchikawa
et al., 1997b) as shown in Figure 3.4. The inner, or Stern layer, consists of counter
ions that are immobilized by the particles’ surface. Outside the Stern layer region lies
the diﬀuse layer, which is made up of ions that are repelled from the particle’s surface
due to the same sign charge. The boundary between the inner and diﬀuse layers is
called the shear plane. The repelled ions in the diﬀuse layer give rise to an electrical
potential that begins at the shear plane and decays with distance (see Figure 3.4). Zeta
potential is deﬁned as the potential diﬀerence between the shear plane and the end of
the diﬀuse layer. This potential is taken as an approximation of the surface charge
of the particle, since it is not possible to measure the surface potential of the particle
47
3. Cement-superplasticizer interactions
Figure 3.4.: Ilustration of interfacial electric double layer formed on cement particle
surface (Uchikawa et al., 1997b; Yoshioka et al., 1997)
itself. Zeta potential can be obtained by measuring charged particles in suspension and
observing their mobility under an electric ﬁeld gradient.
From the potential zeta measurements and for a given particle size and ionic strength
the total interaction potential can be computed as the sum of repulsive potential (i.e.
electrostatic forces) and attractive potential (van der Waals) (see Figure 3.5 (a)),
U
T
= U
R
+ U
A
(3.1)
For further details on the proposed models see (Yoshioka et al., 1997; Uchikawa et al.,
1997a). Holding the zeta potential and particle size constant, while varying the ionic
strength, produces the three basic types of total potential curves depicted in Fig-
ure 3.5 (b). Curve A occurs at low ionic strength and high surface potential and
represents a stable dispersion in which the particles repel each other. The larger the
primary maximum, ψ
max
, the more stable the dispersion. Curve B represents a ﬂoccu-
lated suspension in which particles achieve an equilibrium separation, r
m
, dictated by
the secondary minimum, ψ
sec
. This occurs in systems of moderate ionic concentrations.
As the ionic strength increases, it will eventually reach a critical value and ψ
max
will
disappear, resulting in a coagulated suspension, shown in curve C. Yang et al. (1997)
showed that normal cement paste has an ionic strength that is above the critical con-
centration for coagulation and, according to the DLVO theory, should be coagulated
with an interparticle potential, as depicted by curve C in Figure 3.5 (b). Because of
the high ionic strength in the aqueous phase, the degree of ﬂocculation or coagulation is
not sensitive to the variation of the zeta potential, for zeta potentials between -20 mV
48
3.3. Physical interactions
Repulsive energy (U
R
)
Attractive energy (U
A
)
distance between the
surface of cement particles
U
T
= U
R
+ U
A
(a) (b)
Figure 3.5.: (a) Energy balance according to DLVO theory and (b) illustration of in-
terparticle potentials: A-stable dispersion, B-ﬂocculated suspension, and
C-coagulated suspension (Yang et al., 1997))
and 20 mV (Yang et al., 1997). Furthermore, Yoshioka et al. (2002) found that without
superplasticizer, the zeta-potentials of C
3
S and C
2
S were negative (- 5 mV). However,
those of C
3
A and C
4
AF were positive from + 5 to + 10 mV. Therefore, accelerated
coagulation of cement particles in a plain paste might occur due to their electrostatic
potentials that are opposite to each other. Coagulated particles may retain water, which
is no longer available to enhance ﬂuidity and for the initial hydration reactions (Moir,
2003).
3.3.2. Molecular structure of superplasticizers and mode of action
As mentioned before, cement particles contain several mineral phases of diﬀerent reac-
tivity and their initial hydration will likely generate a surface with important variation in
the surface charge density, both in size and magnitude. These localized surface charges
promote ﬂocculation of hydrating cement particles, but they can be eﬀectively neutral-
ized and separated by the anionic charge of the dispersing admixtures molecules which
are adsorbed on the hydrating cement particles (Chandra and Bjornstrom, 2002).
A variety of dispersing admixtures has been developed and is currently available in the
market. These can be divided in four basic groups according to their chemical struc-
ture as modiﬁed lignosulphonates (LN), sulphonated melamine formaldehyde condensate
(SMF), sulphonated naphthalene formaldehyde condensate (SNF), and polycarboxilate
ethers (PC) also called comb-polymers which contain sulphonic and carboxyl groups
49
3. Cement-superplasticizer interactions
C
C C
C
C C CH CH
SO
3
-
Na
+
OH
CH
2
OH
HO
CH
3
O
(a)
CH
2 CH
2
SO
3
Na SO
3
Na
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
(b)
n
(b)
N
C C
C
N N
NH CH
2
CH
2
NH O
NH
CH
2
SO
3
Na
n
(c)
(c)
C
CH
2
CH
2
CH
z
O
Na
O
CH
2
CH
O
O C
CH
2
O
CH
3
n
(d)
Figure 3.6.: Molecular units of: (a) LN, (b) SNF, (c) SMF, (d) PC (Dransﬁeld, 2003)
(Chandra and Bjornstrom, 2002; Dransﬁeld, 2003). Figure 3.6 shows the simpliﬁed
molecular units of each superplasticizer type. Apart from the superplasticizers of diﬀer-
ent basic groups, there can also be diﬀerences in superplasticizers from the same group
depending upon their synthesis, which inﬂuences upon the molecular weight and chem-
ical conﬁguration, in particular in the case of PC type (Chandra and Bjornstrom, 2002;
Dransﬁeld, 2003).
The dispersion mechanism is dependent upon the type of superplasticizer adsorbed on
the surface. Basically, there are two main types of dispersion mechanisms: electrostatic
repulsion and steric hindrance (Björnström and Chandra, 2003). Some authors discuss
other possible mechanisms, such as depletion eﬀect and tribology eﬀect (Roncero, 2000;
Vikan, 2005).
The relatively high negative zeta potentials obtained when superplasticizers with a
sulfonic group, like LN, SNF and SMF types, were used suggest that the dispersion
mechanism of cement particles is mainly controlled by electrostatic repulsion between
negatively charged particles (Björnström and Chandra, 2003). These molecules all carry
SO
3
Na groups, which in water dissociate into SO
−
3
and Na
+
. The SO
−
3
remains at-
tached to the admixture and carries a strong negative charge (Dransﬁeld, 2003). Part
50
3.3. Physical interactions
(a)
(Figure 4).
(b)
Figure 3.7.: Schematic illustration of (a) electrostatic repulsion and (b) steric stabiliza-
tion
of this charge is used to attach the admixture to the cement but the remainder orien-
tates out from the grain, forming the “Stern Layer” (see Figure 3.4), and repels the
negative charges on admixture adsorbed onto adjacent cement grains, causing them to
move and stay apart (see Figure 3.7 (a)). As well as observing the increased ﬂuidity, the
eﬀect of these superplasticizers can be followed by looking at zeta potential measure-
ments. Yoshioka et al. (2002) reported that in the presence of superplasticizer (Sp), all
cement component minerals were negative in their potential and zeta potential values
varied with the type of Sp. It was reported that the SNF and SMF superplasticizers
with largest molecular weight gives the largest negative zeta potential (Björnström and
Chandra, 2003). The LN, SNF and SMF have a lower molecular mass than the PC
type and have no long side chains. The absence of long side chains provide the smaller
sulfonated polymers with a higher charge density than PC polymer (Björnström and
Chandra, 2003). As can be observed in Figure 3.8, the charge on the surface of particles
from diﬀerent cement types is positive before the Sp addition but then goes negative as
the Sp adsorbs. Furthermore, the magnitude of the change in the charge varies with the
type of Sp. It was much greater with Sp A, which is of LN type. Sp B and Sp C were
51
3. Cement-superplasticizer interactions
Figure 3.8.: Zeta potential of diﬀerent cement types and mineral additions, with and
without superplasticizer. SP A is of LN type and SP B and C are of PC
type (Nunes et al., 2008)
both of PC type but with diﬀerent molecular structures, based on diﬀerent performance.
It should be pointed that the zeta potential results presented in Figure 3.8 does not
necessarily correspond to the ﬂuidities of cement paste and concrete. The measuring
method of zeta potential used in this work was the electrophoretic method, which re-
quires diluted concentration of particles in suspension because the migration speed of
dispersed particles to which voltage is applied is directly observed (Nunes et al., 2008).
This means it is unsuitable for practical cement paste with a w/c of 0,5 or less be-
cause it requires the concentration of particles of 1 %, in volume, or less but also it has
shortcomings including poor reproducibility and low accuracy (Uchikawa et al., 1997b).
In spite of these shortcomings, these zeta potential results can be used in comparative
terms and are helpful to identify the mode of action of diﬀerent superplasticizer types.
Based on DLVO theory, several authors suggested that the zeta-potential of cement must
be less than -15 to -25 mV for stable dispersion (Uchikawa et al., 1997b; Yang et al.,
1997; Yoshioka et al., 2002). Thus, based on the zeta potential results obtained with Sp
A it can be expected that the electrostatic repulsion is the main repulsion mechanism
of Sp A. In contrast, the zeta potentials obtained when using a PC superplasticizer
types were always higher than -15 mV, in similar cement-based dispersions. Sp C led to
even higher zeta potential values than Sp B for a superior performance. In Figure 3.9
it can be observed that the dispersing action of Sp C is stronger than Sp B leading
to mixtures with lower yield stress and lower plastic viscosity, for the same dosage
(Sp/p is the solid weight of superplasticizer by unit weight of powder materials). This
52
3.3. Physical interactions
Figure 3.9.: Bingham parameters of cement pastes incorporating Sp B and Sp C,
with varying dosages of Sp (CEM I 52,5 R; limestone ﬁller; w/c=0,35;
w
f
/w
c
=0,39; 15 min)
means the mechanism by which cement particles are dispersed can not be attributed to
electrostatic repulsion, in the cases of Sp B and C. Uchikawa et al. (1997b) have utilized
an atomic force microscope to measure the interactive force between the surface of the
cement clinker and an adsorbed admixture, and compared this with the zeta potential
measured by the electrokinetic sonic amplitude method. They concluded that steric
hindrance played an important role in the dispersion of cement pastes, in the case of PC
superplasticizer type. The molecules of PC type seem to bond chemically through their
carboxyl (sulfonate and/or hydroxyl groups) which carries a moderate negative charge
that is used to attach the admixture to the cement (Yoshioka et al., 1997). The long
side chains (polyether groups) orientate away from the cement surface but will resist
becoming entangled with the chains attached to an adjoining cement grain, thus keeping
grains apart (Dransﬁeld, 2003) (see Figure 3.7 (b)). Zeta potential values close to zero
are reported in the literature in the case of PC type superplasticizers, depending on
their molecular structure (Sakai et al., 2003).
The dispersion of cement particles through electrostatic repulsion that results from the
adsorption of LN, SNF or SMF can be understood with the aid of DLVO theory (see
Figure 3.10 (a)) (Neubauer et al., 1998; Yang et al., 1997). In the case of PC, the
total interparticle potential energy (see Figure 3.10 (b)) can be calculated based on an
assumed model for the adsorption of PC molecules, which accounts for long-range Van
der Waals interactions (U
A
), steric hindrance (U
S
), and electrostatic stabilization (U
S
)
(Yoshioka et al., 1997), given by
U
T
= U
R
+ U
A
+ U
S
(3.2)
53
3. Cement-superplasticizer interactions
(a) (b)
Figure 3.10.: Examples of total interparticle energy curves when the main dispersion
mechanism is (a) electrostatic repulsion (described by DLVO theory) (b)
steric hindrance (n is the number of ethylene oxide units in the graft
chain)(Yoshioka et al., 1997)
In this equation (U
R
+U
A
) constitute the DLVO theory. The steric hindrance eﬀect (U
S
)
was given by Evans and Napper (Yoshioka et al., 1997). According to this model, the
PC molecules are assumed to be adsorbed on the cement particles, in the conﬁguration
shown in Figure 3.11, with the polyethylene oxide chain stretching into the solution. The
existence of the main chain is neglected for the calculation of the steric hindrance eﬀect.
Input parameters for this model include the number of ethylene oxide units in the graft
chain (value n in Figure 3.10 (b)), the eﬀective chain length, the distance between two
adjacent graft chains and the molecular weight of one graft chain. The problem, which
is encountered in the application of these models very often, is the non-availability of
the technical data of the Sp molecules.
Hesselink et al. (referred in (Yoshioka et al., 1997)) suggested that suspended particles
coagulate when the minimum of the potential curve becomes smaller than approximately
−5 kT. The particles that are in a weak ﬂocculation have a tendency to disperse again,
with the aid of mechanical energy, when the value increases above approximately −5
kT.
Superplasticizers may adsorb also on inert powders which are added to concrete, such
as ﬂy ash, limestone, silica fume and clays (Plank and Hirsch, 2007). The reason for
this is electrostatic interaction between the admixtures and the charged surfaces of these
powders. As can be observed in Figure 3.8, without superplasticizer, the measured zeta
potential for limestone ﬁller was positive while for ﬂy ash it was negative. Other authors
54
3.4. Chemical interactions
main chain
graft chains
Figure 3.11.: Example of a PC type polymer molecule (Sika ViscoCrete®) adsorbed on
cement grain surface
also found negative values for zeta potential of ﬂy ash without superplasticizer (-15 and
-21 mV) which became even more negative (-49 and -63 mV) in the presence of a SNF
superplasticizer (Termkhajornkit and Nawa, 2004). The partial substitution of cement
by limestone ﬁller was found to be beneﬁcial to reduce the potential zeta until a certain
percentage, depending on cement type (see Figure 3.12). Thus, improved ﬂuidity can
be expected with the partial substitution of cement by limestone ﬁller.
3.4. Chemical interactions
The physical interactions described above, involved in the deﬂocculation of cement par-
ticles when superplasticizers are added, play the most important role with regard to
superplasticizer action, but also chemical interactions are suggested to explain changes
on the behaviour of cement paste and concrete during the induction period (Flatt, 1999;
Roncero, 2000).
3.4.1. Preferential adsorption of Sp on speciﬁc surface sites (the
role of ettringite)
Previous studies of superplasticizer adsorption on the pure cement clinker phases re-
vealed that much higher adsorption occurs on the aluminate and ferrite than on the
55
3. Cement-superplasticizer interactions
Figure 3.12.: Eﬀect of the partial substitution of cement by limestone ﬁller on zeta
potential results, in the presence of Sp B, for diﬀerent cement types (Nunes
et al., 2008)
silicate phases, due to positive zeta potential values (Yoshioka et al., 2002). In a study
carried out by Plank and Hirsch (2007) on early cement hydration phases, ettringite
1
and monosulfate
2
showed positive zeta potentials, + 4,15 and + 2,84, respectively, while
syngenite
3
, portlandite
4
and gypsum
5
showed zero or negative zeta potentials. The ad-
sorbed amount of superplasticizer strongly depends on the existence of a positive zeta
potential of the hydration phase. At comparable speciﬁc surface area, ettringite shows 2
to 4 times more polymer adsorbed per surface area than monosulfate and the adsorption
of superplasticizers on portlandite and gypsum was negligible (Plank and Hirsch, 2007).
This emphasizes the importance of ettringite for cement–superplasticizer interaction.
Superplasticizers may also adsorb on C-S-H phases, however, the amount of polymer
adsorbed per unit area is relatively small (Plank and Hirsch, 2007). Based on these
results, Plank and Hirsch (2007) proposed a mosaic structure for the hydrating cement
grains with uneven distribution of polymer molecules on its surface, concentrated on
spots where ettringite crystallizes, as shown in Figure 3.13.
1
[Ca
6
Al
2
(OH)
12
] (SO
4
)
3
·26H
2
O=Aft (in order to reﬂect the variable composition of ettringite formed
by mixtures of C
3
A and C
4
AF ettringite is often referred to as Aft, which stands for alumino-ferrite
trisulfate hydrate)
2
[Ca
4
Al
2
(OH)
12
] (SO
4
) · 6H
2
O=Afm
3
K
2
SO
4
· CaSO
4
· H
2
O
4
Ca(OH)
2
5
CaSO
4
· 2H
2
O
56
3.4. Chemical interactions
Figure 3.13.: Uneven polymer distribution on the surface of a cement grain (Plank and
Hirsch, 2007)
3.4.2. Polymer adsorption and absorption
It has been shown that SNF and PC type superplasticizers (even those with a high
density of very long side chains) not only adsorb on the surface of tricalcium aluminate,
but also intercalate into its hydrate phase and form an organo-mineral compound (Plank
et al., 2006). For this reason it is important to diﬀerentiate polymer consumption
from polymer adsorption. The term polymer ‘consumption’ was suggested to group
adsorption and absorption in the same term (Flatt and Houst, 2001) (see Figure 3.14).
Thus, the superplasticizer added to a cement suspension can be divided into three parts:
consumed by chemical reactions (during formation of Aft and C-S-H); adsorbed onto the
hydrating cement particles (not integrated into organo-mineral products); remaining in
the aqueous phase (Flatt and Houst, 2001).
The problem of polymer intercalation into hydrate phases is that this material is lost
for adsorption and does not become eﬀective as dispersing admixture. This can reﬂect
in the required dosages and/or the dispersing eﬀect as well as in the ﬁnal properties
of hydrated cement such as compressive strength (Plank et al., 2006). The amount of
polymer, which is consumed by the early reactions, depends both on the polymer and
the cement. Flatt and Houst (2001) introduced the concept of reactivity of a cement
towards a superplasticizer, which is illustrated in Figure 3.14. In order to obtain the
same ﬂuidity, more superplasticizer was needed for cements having a high content of
C
3
A and/or a high cement ﬁneness (Griesser, 2002). C
3
A seems to have a stronger
inﬂuence on ﬂuidity compared to C
4
AF (Griesser, 2002). Further, slight weathering of
cement decreases hydration reactivity and superplasticizer dosage required for a given
ﬂuidity (Sakai et al., 2003). A very reactive cement would consume a lot of polymer
by intercalation, therefore giving a lower workability at equal dosages. For economic
reasons it is of interest to reduce the reactivity of cements.
In the next points, several ways to change the reactivity of cements towards a polymer
will be discussed, considering diﬀerent aspects related to cement and superplasticizer
57
3. Cement-superplasticizer interactions
Active Polymer
Lost Polymer
Less Reactive Cement More Reactive Cement
Figure 3.14.: Schematic interpretation of polymer adsorbed and absorbed (intercalated
into hydrate phases) and situations of high and low reactivity of cement
towards a superplasticizer (Flatt, 1999)
type.
3.4.3. Inﬂuence of adding time
It was found that delayed adding time of SNF and SMF type superplasticizers decreased
the superplasticizer consumption compared to direct adding, or less superplasticizer was
required for equal performance (ﬂuidity) (Hanehara and Yamada, 1999). It was sug-
gested that the superplasticizer should be added 2 minutes after water addition, which
coincides with the beginning of the dormant stage (Roncero, 2000). Plank and Hirsch
(2007) compared the consumed amounts for diﬀerent superplasticizers when added at
the beginning and after completion of ettringite crystallization. As can be seen in Fig-
ure 3.15, the consumed amounts of SMF and SNF are approximately 50% less with
delayed adding time. Uchikawa et al. (1995) veriﬁed these ﬁndings by measuring the
thickness of the hydrates formed on a polished clinker surface, which was dipped into
an aqueous solution of SNF type superplasticizer, in both direct and delayed adding
modes. In the case of direct adding the adsorbed hydrate layer thickness of C
3
S and
the interstitial phase was about 50 and 300 nm, respectively. At delayed adding time
mode, the thickness of the adsorbed hydrates was 20 nm both for C
3
S and the intersti-
tial phase, and was compatible with the polymer size (referred in (Flatt, 1999)). It was
also suggested that delayed adding time reduces the adsorption on C
3
A and enhances
the adsorption on silicate phases, which explains enhanced ﬂuidity. According to Flatt
(1999), in direct adding mode, a ﬁrst layer of coprecipitate is formed, which might be
expected to have dielectric properties similar to cement. Thus, the plane of origin for the
van der Waals force would be shifted from the surface towards the basis of the polymer
58
3.4. Chemical interactions
Figure 3.15.: Adsorbed (or more correctly ‘consumed’) amounts of superplasticizers
(PMS and BNS corresponds to SMF and SNF, respectively) on ettringite
in case of direct and delayed adding time (Plank and Hirsch, 2007)
layer. Provided enough polymers are added an ultimate layer might be able to adsorb
and only this fraction of the polymer would be able of inducing electrostatic and steric
repulsion (see Figure 3.16) (Flatt, 1999). In delayed adding time mode, most aluminates
would form ettringite, before polymer is added.
As can be observed in Figure 3.15 the consumed amounts of PC is generally lower than
the consumed amounts of SNF/SMF types, in both direct and delayed adding time
modes. Furthermore, for PC type superplasticized cements both the consumption of
superplasticizer molecules and the ﬂuidity was found to be less inﬂuenced by delayed
adding time (Plank and Hirsch, 2007). Similar results were reported by other authors
(Hanehara and Yamada, 1999). This implies that less PC type superplasticizers inter-
calates into hydrate phases. This may be due to a reduced hydration activity caused
by the superplasticizer molecules, due to the lower ionic activity compared to SMF and
SNF type superplasticizers (Plank and Hirsch, 2007) or due to the larger size of PC
molecules (Flatt, 1999).
3.4.4. The role of sulfate and alkalis
Various researchers reported the competition of SO
3
originating from SNF and SMF
and sulfate ions present in the pore water for the same reactive sites on the hydrat-
ing cement surface, particularly on C
3
A. It was found that the addition of soluble
sulfates (K
2
SO
4
, Na
2
SO
4
, hemihydrate) reduced the consumed amount of SNF type
superplasticizer (Griesser, 2002; Yamada et al., 2001b). If more sulphates are available,
more ettringite will be formed and, consequently, fewer polymers are incorporated into
hydrates, during the ﬁrst minutes of cement hydration. As proposed by Flatt (1999)
(see Figure 3.17), the consumption of polymer is decreased by higher availability of
59
3. Cement-superplasticizer interactions
Direct addition
Delayed addition
Active polymer layer
Ettringite
Coprecipitate, gel or ettringite highly intercalated
Cement
Figure 3.16.: Schematic interpretation of the inﬂuence of adding time on the amount of
polymer consumed (Flatt, 1999)
sulfate ions, whether directly added or coming from the cement, because it minimizes
the amount of polymer lost in organo-mineral products so that at equal dosage the su-
perplasticizer eﬃciency is higher. Since the dissolved amount and the dissolution rate
of alkali sulfates is considerably higher compared to the one of calcium sulfates, the
former may signiﬁcantly inﬂuence the rheological properties of superplasticized cement.
The existence of optimum soluble alkali content was reported (Jiang et al., 1999; Vikan,
2005). Cements with less than the optimum soluble alkali content showed signiﬁcant
increases in ﬂuidity when Na
2
SO
4
was added while cements with more than the opti-
mum soluble showed slightly decreased ﬂuidity with the addition of Na
2
SO
4
. In the
presence of SNF, the optimum soluble alkali content for high initial ﬂuidity and low loss
of ﬂuidity with time was found to be in the order of 0,4-0,5% (Na
2
O)
equivalent
6
(Jiang
et al., 1999). It was suggested that an excessive amount of alkali sulfate compressed
the electric double layer, which could explain the decrease in ﬂuidity of cement paste
(Zhang, 2001).
It should be noted that, depending on the clinker SO
3
content, alkalis in cement can be
present as alkali sulphates (K
2
SO
4
or Na
2
SO
4
) or these may enter into solid solution
in the aluminates and silicate phases. The ratio of sulphur to total alkali determines
the quantity of alkali sulphates in a clinker. When a clinker contains relatively large
amounts of SO
3
, a substantial fraction of alkalis goes into solution within a few minutes.
In low SO
3
clinker, Na
2
O and K
2
O are incorporated preferentially into the C
3
A phase,
but also into C
2
S phase of Portland clinker. Therefore, although the cements may have
similar SO
3
content and total alkali contents, the amount of alkalis readily soluble in
6
(Na
2
O)
equivalent
is obtained from cement alkalis content as (Na
2
O + 0, 66K
2
O)
60
3.4. Chemical interactions
Low sulfate content
High sulfate content
Cement
Active polymer layer
Ettringite
Coprecipitate, gel or ettringite highly
intercallated
Figure 3.17.: Schematic interpretation of the inﬂuence of sulfate content (below the op-
timum dosage) on the amount of consumed polymer (Flatt, 1999)
them can vary widely (Jiang et al., 1999).
Various authors referred in (Hanehara and Yamada, 1999) found that alkaline sulphate
in cement had the most signiﬁcant eﬀects on the dispersing action of PC type superplas-
ticizers. The ﬂuidity was lowered by increasing the amount of alkali sulfate. This was
attributed mainly to the reduction of the amount of the admixture adsorbed on cement
caused by adding the sulfate ions (Hanehara and Yamada, 1999; Yamada et al., 2001b).
It was concluded that there is a competitive adsorption between the carboxylic group
of the PC polymer and sulfate ions present in the pore water for the same reactive sites
on the hydrating cement surface. Also, it was found that a high concentration of ions
(sulfate or chloride ions) in the mixing water can shrink the side chains of PC polymer
(responsible for the steric repulsion), thus reducing the dispersion action (Yamada et al.,
2001b).
3.4.5. Fluidity loss with time and inﬂuence of temperature
Fluidity changes with elapsed time and temperature can also be discussed based on
the concept of reactivity of cement towards a superplasticizer. With the progress of
hydration, the surface area of cement particles increases, thus more polymer is consumed
inducing loss of ﬂuidity. On the other hand, sulfate ion concentration simultaneously
decreases with elapsed time, which allows for higher adsorption of polymer molecules
(if polymer molecules remain in the solution phase) inducing higher ﬂuidity. Balance of
these two eﬀects determines the occurrence of ﬂuidity retention, ﬂuidity loss or ﬂuidity
gain. Thus, to realize good performance for ﬂuidity retention, a suitable amount of
61
3. Cement-superplasticizer interactions
Sp must remain in the solution phase (Sakai et al., 2003). At lower temperatures, a
smaller increase in speciﬁc surface of cement paste and a larger decrease in sulphate ion
concentration reduce the reactivity, resulting in ﬂuidity gain (Yamada et al., 1999). On
the contrary, at higher temperatures, larger increases in speciﬁc surface of cement paste
and smaller decreases in sulphate ion concentration result in higher reactivity, inducing
ﬂuidity loss (Yamada et al., 1999).
PC type superplasticizers maintain workability over a long time range. Sakai and Dai-
mon (referred in (Flatt, 1999)) attribute this to the fact that the polyethylene side chains
of the polymer molecules can stretch out and induce a steric eﬀect while hydration layers
grow from the surface.
3.4.6. Inﬂuence on hydration rate and hydration products
As may be expected, presence of organic admixtures which can interfere with nucleation
and/or growth processes will inﬂuence hydration reaction rate, reaction products, or
both. In general, all superplasticizers retard hydration of cement compared to a plain
cement paste. Diﬀerent mechanisms were proposed to explain inhibition of setting (set
retardation): adsorbed superplasticizer molecules hinder diﬀusion of water and calcium
ions at the cement-solution interface; formation of complex ions between Ca
2+
and
superplasticizer (decreasing the concentration of Ca
2+
in the solution and decreasing
the formation of portlandite); and the dispersive action of superplasticizers changes
growth kinetics and morphology of hydrate phases (Roncero, 2000; Winnefeld et al.,
2007). According to Winnefeld et al. (2007), retarding eﬀect of PC type Sps is probably
not caused by interaction with dissolved calcium ions, but due to changes in nucleation
and growth kinetics of the hydrate phases.
Several authors reported changes in size and morphology of ettringite (Plank and Hirsch,
2007; Prince et al., 2003). In the presence of superplasticizers, the ettringite crystals
are much smaller. In addition, the morphology changes from long and thin to short and
compact needles. This was explained by preferential adsorption of superplasticizers on
ettringite crystals inhibiting crystals growth (Plank and Hirsch, 2007).
3.5. Incorporation of viscosity agents
The incorporation of a viscosity agent in the concrete mixture can interfere in the
cement-superplasticizer interactions. Viscosity agents (VA), also known as viscosity-
modifying, viscosity-enhancing, anti-washout or stabiliser agents, are used in SCC ap-
plications to:
62
3.5. Incorporation of viscosity agents
Figure 3.18.: Target area of SCC mortars with and without a viscosity agent (Nawa
et al., 1998)
• enhance homogeneity of concrete (improve segregation resistance, reduce bleed-
ing, decrease surface settlement, counteract blocking of aggregates near obstacles)
(Grünewald and Walraven, 2004; Khayat, 1998; Phyﬀeroen and Lockwood, 1998);
• decrease sensitivity of SCC (mitigate the eﬀects of variations in materials and pro-
portions, like surface moisture content and grading of ﬁne aggregate, but also ef-
fects of changes in concrete temperature) (Grünewald and Walraven, 2004; Khayat,
1998; Phyﬀeroen and Lockwood, 1998);
• design SCC mixtures which are not stable without a VA (to allow higher wa-
ter/powder ratios, lower paste contents and the use of a wider range of materials
such as gap-graded aggregates, manufactured sands, ﬁbres and lightweight aggre-
gates) (Grünewald and Walraven, 2004; Khayat, 1998; Phyﬀeroen and Lockwood,
1998) (see Figure 3.18).
Nawa et al. (1998) classiﬁed the VA’s, used to enhance the cohesion and stability of
cement-based materials, as presented in Table 3.2. Their mechanism of action varies
largely depending on the type (see Table 3.2). These products are available either in
powder- or in liquid-based form. Admixtures combining a superplasticizer and a VA
have been developed, especially in products developed speciﬁcally for SCC applications.
The advantage of these products is that no extra storage and dosing system for VA is
required. But, for mixture optimization purposes it is better to add the SP and VA
separately (Grünewald and Walraven, 2004).
According to Nawa et al. (1998) VAs may be broadly divided into two types: adsorptive
and non-adsorptive. The functional group in the molecular structure of a VA is attracted
to the cement surface in case of adsorptive type; while in the case of non-adsorptive type,
it is attracted with water (Nawa et al., 1998). Consequently, adsorptive type VAs hinder
the interaction of superplasticizers on cement surface. As can be observed in Figure 3.19,
63
3. Cement-superplasticizer interactions
Table 3.2.: Types of VA and mode of action (Nawa et al., 1998; Phyﬀeroen and Lock-
wood, 1998)
Type
- cellulose-based polymers
Soluble - acrylic-based polymers
in - glycol-based polymers
water
Not soluble - bio-polymers
in - inorganic materials of
water high surface area
Mode of action
adsorptive - adsorb on the surface of cement particles and
increase viscosity due to interparticle attraction forces;
non-adsorptive - do not adsorb on the surface of cement particles
but increase viscosity by action of linking
between its own molecules;
- adsorb water, swell, and impart viscosity
to the mixture
the adsorbed amount of superplasticizer decreases with increasing adsorptive VA con-
tent. In this case, both yield stress and viscosity of cement paste will be aﬀected with
the introduction of VA. The increase in yield stress typically must be oﬀset with addi-
tional water or superplasticizer (Yahia and Khayat, 2001b). On the other hand, mainly
paste viscosity increases with the incorporation of non-adsorptive VAs. Most VAs used
in concrete are adsorptive (Nawa, et al., 1998). Mixtures containing VA also exhibit
enhanced shear-thinning and/or thixotropic behaviour (Grünewald and Walraven, 2005,
2004; Khayat, 1998; Lachemi et al., 2004). This is a result of association and intertwin-
ing of polymer chains (Khayat, 1998). According to Khayat (1998) the VA polymers
themselves develop attractive forces (association) and thereby block the motion of wa-
ter causing viscosity increase. The polymer chains can also intertwine especially at low
shear rates and high concentrations, but break apart, stretch and orientate in the di-
rection of ﬂow at higher shear rates, hence resulting in shear-thinning behaviour. This
shear-thinning and/or thixotropic behaviour ensures static stability (due to high viscos-
ity at low shear rates) without interfering much with the required energy for processes
like mixing, pumping and casting (due to lower viscosity at high shear rates).
Some incompatibilities were reported between cellulose derivatives and SNF superplas-
ticizers (Khayat, 1998). SNF-type superplasticizers are compatible with acrylic-based
VAs. SMF- type superplasticizers are compatible with cellulose-, acrylic- and glycol-
based VAs. PC-type superplasticizers are compatible with cellulose- and glycol-based
VA’s (Grünewald and Walraven, 2004). Welan gum (a biopolymer commonly used in
cement-based materials) is compatible with both SMF and SNF types of superplasticizer
(Khayat, 1998).
Besides aﬀecting rheology the incorporation of a VA can alter cement hydration and
hardened concrete properties. The extent of these changes depend on the mixture
composition, the type of superplasticizer, the type of VA, the required extra dosage of
SP. Welan gum and cellulose derivative VAs may delay concrete setting times, while
acrylic-based VAs generally do not aﬀect setting time (Khayat, 1998). VAs can aﬀect
64
3.6. Molecular structure of PC type superplasticizers
Figure 3.19.: Adsorbed amount of superplasticiser on the cement surface in the presence
of adsorptive and non-adsorptive VAs (Nawa et al., 1998)
the air-void system of SCC, which can have consequences in terms of the quality of
surface ﬁnishing, stiﬀness and strength of concrete and/or durability. In general, the
interface zone between paste and aggregates and between paste and reinforcing bars is
improved (Khayat, 1998). To sum up, when a VA is to be used, diﬀerent types should be
considered and their eﬀects should be studied in conjunction with the superplasticizer,
cement and other additions to come up with an optimized mixture that satisﬁes all
performance requirements (Yahia and Khayat, 2001b).
3.6. Molecular structure of PC type superplasticizers
Nowadays, there is a wide use of PC type superplasticizers because of their superior
performance in dispersing cement particles and maintaining ﬂuidity in time, especially
for high ﬂuidity concretes like the case of self-compacting concrete. The polycarboxylate
polymer is made up of a main chain and polyoxyethylene side chains (see Figure 3.11).
These long side chains are responsible for the steric hindrance eﬀect. It is believed
that the adsorption of this kind of polymer on cement particles occurs via carboxylic
(sulfonate and/or hydroxyl) groups from the main chain. A characteristic of this type
of SP is that its chemical structure has the potential to be modiﬁed. The variations in
type and length of the main and side chain of PC-type superplasticizers yield to a broad
variety of new products with very variable properties (see Figure 3.20). Therefore,
various PC type products are available in the market for speciﬁc applications. Sp B
and Sp C, presented before, are good examples of this. Sp B is recommended for in-
situ applications with prolonged transportation time while Sp C is more indicated for
precast applications where enhanced ﬂuidity and not too prolonged setting times are of
65
3. Cement-superplasticizer interactions
Figure 3.20.: Schematic representation of PC type molecules with varying molecular
structures (n is the number of ethylene oxide units; MCL is the main chain
length; SCL is the side chain length) (Plank and Sachsenhauser, 2006)
interest. The rheological results presented in Figure 3.9 conﬁrm that Sp C has a stronger
dispersing capability (resulting in lower yield stress and lower plastic viscosity, for the
same dosage, in solid content); but cement pastes incorporating Sp C lose ﬂuidity more
rapidly than cement pastes incorporating Sp B (see Figure 3.21).
The inﬂuence of PC type molecular structure on the rheological properties of cementi-
tious materials has been a subject of investigation. The main eﬀects of chemical structure
parameters on PC type superplasticizer performance are summarized in Table 3.3. It
was found that the adsorption behaviour of Sp determines the rheology and setting of
cement paste, mortar and concrete. Adsorption is related to the charge density of poly-
mer. According to Winnefeld et al. (2007) the charge density of the polymer increases
with decreasing side chain density and with decreasing side chain length, leading to an
increasing amount of free carboxylic groups. Furthermore, polymer fractions with a high
molecular weight adsorb preferentially on the cement particles. Various authors found
that PC type superplasticizers with higher molecular weight adsorb stronger compared
to polymers with lower molecular weight and same molecular architecture (Winnefeld
et al., 2007). As can be observed in Table 3.3, there seems to be no agreement con-
cerning the eﬀect of side chain length. The minor eﬀect of side chain length found by
Winnefeld et al. (2007) was attributed to the conformation of the side chains, which is
not stretched but more ‘‘mushroom’’-like especially in aqueous solutions with high ionic
strengths.
The retarding eﬀect seems to be determined by the coverage of the surface of the cement
with PC polymer molecules. In the case of a PC polymer with long chains most of the
66
3.6. Molecular structure of PC type superplasticizers
Figure 3.21.: Evolution of Bingham parameters with time of cement pastes incorporating
Sp B ((Sp/p)
solid
= 0, 25%) and Sp C ((Sp/p)
solid
= 0, 175%) (CEM I 52,5
R; limestone ﬁller; w/c=0,35; w
f
/w
c
=0,39; Temperature=20
◦
C)
mass of the polymer is concentrated in the non-adsorbing backbone. Thus, the coverage
of the surface of the cement is much less compared to a PC polymer with short side
chains. A cement particle with a less covered surface should exhibit a faster setting
(Winnefeld et al., 2007). Retardation increases also with PC dosage and the inﬂuence
of PC dosage on retardation is more pronounced for shorter side chains and for lower side
chain densities (Winnefeld et al., 2007). An increase of the molecular weight of PC had
only a slight eﬀect on setting times (Winnefeld et al., 2007). Other authors found that
the setting time of cement paste depended on the ionic functional group concentration
in the aqueous phase of the cement paste (Kirby and Lewis, 2004; Yamada et al., 2000).
Winnefeld et al. (2007) also found that increasing side chain length and increasing side
chain density of PC polymer lead to higher early strengths (16 h, 1 day).
Yamada et al. (2001a) suggested that the molecular structures with the longer main
chain, the longer side chain and the higher COOH ratio are more resistant to variations
in the SO
2−
4
content of cement. Among these parameters, the ratio of carboxylic group in
the trunk chain (COOH ratio) was found to be the most important factor (Yamada et al.,
2001a). A higher tolerance for the ﬂuctuation of sulfate ion concentration is of interest
to improve mixtures robustness but it can be contradicting with other performance
requirements, like ﬂuidity retention.
Based on the eﬀects discussed above one can speculate about the polymer structure
diﬀerences between Sp B and Sp C that can be responsible for the higher ﬂuidity, faster
ﬂuidity loss and shorter setting times of cement pastes incorporating Sp C. Compared
to Sp B, Sp C probably has lower density of side chains (to increase the adsorption) and
67
3. Cement-superplasticizer interactions
Table 3.3.: Summary of the eﬀect of chemical structure parameters on the performance
of superplasticizer
higher less shorter higher
ﬂuidity ﬂuidity loss setting time early strength
side chain length longer
a
shorter
a
longer
a, b
longer
b
minor eﬀect
b
main chain length shorter
a
longer
a
side chain density lower
b
higher
b
higher
b
ionic group content higher
a
lower
a, c
molecular weight higher
b
minor eﬀect
b
according to:
a
(Yamada et al., 2000);
b
(Winnefeld et al., 2007);
c
(Kirby and Lewis, 2004)
longer side chains (to reduce retardation) which are responsible for the faster ﬂuidity
loss.
68
4. SCC mix-design and properties
4.1. Introduction
SCC is deﬁned primarily in terms of its fresh properties, therefore, the characterization
and control of fresh properties are critical to ensure successful SCC performance. Fresh
properties inﬂuence not only workability but also hardened properties like strength
and durability. Furthermore, SCC is a complex material exhibiting several sensitive
interactions between the constituent materials and further work is needed to better
understand the eﬀect of mixture parameters governing its fresh properties.
This chapter ﬁrst describes the fresh properties that are relevant for the study and
production of SCC and presents available empirical test methods, focusing mainly on
those for which CEN is preparing standards. Speciﬁcations for SCC depending on
application type are also considered. Then, the possible diﬀerences between hardened
properties of SCC and conventional concrete and the applicability of currently existing
design rules are discussed. After that, an overview of existing mix-design methods is
given and the choice for the Experimental Design approach is justiﬁed. Finally, the
Japanese SCC-designing method and the Experimental Design approach are described
in further detail.
This chapter provides the mixtures formulation, the mix-design procedures and the test
methods for the studies presented in Chapters 5, 6 and 7.
4.2. Fresh properties and empirical tests
4.2.1. SCC fresh properties
The key workability requirements for SCC are ﬁlling ability, passing ability and segre-
gation resistance (BIBM et al., 2005; Concrete Society, 2005). The former describes the
ability of concrete to ﬂow under its own weight and completely ﬁll formwork. Passing
ability describes the ability of concrete to ﬂow through conﬁned conditions, such as
the narrow openings between reinforcement bars. Segregation resistance (or stability)
69
4. SCC mix-design and properties
describes the ability of concrete to remain homogeneous both during transport and plac-
ing (in dynamic conditions), and after placing until setting (in static conditions) (BIBM
et al., 2005; Concrete Society, 2005). Additional information can be given about concrete
mix viscosity, which is also important in understanding passing ability and segregation
resistance, but is not essential for deﬁning a fresh concrete as being self-compacting
concrete.
Various test methods are available to measure these properties; however, no test method
exists to measure all of these properties at once. Given that these properties are in-
terrelated, most tests indirectly measure more than one property at a time, as will be
presented in paragraph 4.2.2. Requirements for SCC workability can vary signiﬁcantly
depending on the application type - this will be discussed in paragraph in 4.2.3.
4.2.2. Standard (empirical) test methods
Traditional workability test methods like ‘slump-test’, ‘ﬂow table’, ‘Vebe’ or ‘degree of
compactability’ are not adequate for describing SCC fresh state properties. According to
EN-206-1:2007 all SCCs fall into S5 class (slump) and F6 class (ﬂow-table). None of these
tests is able to distinguish the SCC mixtures in terms of ﬁlling ability, passing ability
and segregation resistance. Thus, a wide range of test methods have been developed
to measure and assess the fresh properties of SCC. Many of the existing tests were
developed in commercial secrecy for speciﬁc applications and there had been no attempt
to ensure that they were more generally applicable. Due to the lack of standardization
of these test methods, the dimensions and details of tests found in literature can vary,
inﬂuencing test results. Daczko (2003) lists dimensions of L-boxes, U-boxes, and J-rings
reported by various researchers in literature. Furthermore, no agreement was reached
on which test was the most suitable for general purposes. This hindered the increased
use of SCC in general construction, since it was diﬃcult to validate mix-designs and
to write performance-based speciﬁcations. The establishment of a standardised test (or
tests) was therefore an essential prerequisite to realise potential beneﬁts of SCC and
facilitate its widespread use in general construction.
Within the EU funded “Testing-SCC” project, 10 out of 23 potential tests were selected
for detailed evaluation by a combination of laboratory tests, full-scale site trials and
rheological studies (ACM Centre, 2005). From these results, a number of tests were
identiﬁed as the most promising for use in the laboratory for mix design and development
or in-site acceptance and quality-control. In addition, those tests were assessed for
precision in a round-robin programme involving 23 laboratories throughout Europe in
order to recommend the most suitable for CEN codiﬁcation. One of the main conclusions
of this project was that there is no single universal test, which can detect diﬀerences
70
4.2. Fresh properties and empirical tests
Table 4.1.: Standardised test methods, assessed fresh property and consistency classes
(ACM Centre, 2005)
Test Test result Fresh property Consistency classes
Slump-ﬂow
a
SF (mm) primarily to SF1 550 to 650
assess ﬁlling ability SF2 660 to 750
SF3 760 to 850
t500 (s) primarily to VS1 ≤2
assess viscosity VS2 3 to 6
VS3 > 6
V-funnel
a
tv (s) partially indicates ﬁlling VF 1 < 9
ability and blocking VF 2 9 to 25
L-box
b
PA primarily to assess PL1 ≥0,8 with 2 rebars
passing ability PL2 ≥0,8 with 3 rebars
J-ring
a
BJ (mm) primarily to assess PJ1 ≤10 with 12 rebars
passing ability PJ2 ≤10 with 16 rebars
SFJ (mm)
T500J
Sieve SR (%) to assess SR1 ≤20
stability
a
segregation resistance SR2 ≤15
a
suitable for laboratory and site use;
b
suitable for laboratory use
between good and bad SCC mixes and is capable of measuring all three key properties
(i.e. ﬁlling ability, passing ability and segregation resistance). Thus, a combination of
tests might be required to fully characterize a mix. Based on the recommendations of
The European project Group, the standards of ﬁve tests methods are being prepared
by CEN, namely, Slump-ﬂow, L-box, V-funnel, J-Ring and Sieve stability. These tests
are listed in Table 4.1 along with the property(ies) assessed. These methods are widely
used across Europe and for each of them speciﬁcation classes were assigned, as shown
in Table 4.1, (ACM Centre, 2005).
Slump ﬂow test
Slump-ﬂow test is a sensitive test that should normally be speciﬁed for all SCCs. It
describes the ﬂowing ability of a fresh mix in unconﬁned conditions. Test results are the
slump-ﬂow diameter (SF, expressed to the nearest 10 mm ) and the time needed to reach
the 500 mm diameter (t500, expressed to the nearest 0,5 s), which give an indication of
ﬁlling ability and relative viscosity of SCC, respectively. Because the slump ﬂow result
can vary by changing the nature of the baseplate material, a metal surface has been
speciﬁed; which should be moistened and without excess water (see Figure 4.1). This
test can be performed by one single operator with the inclusion of a steel collar on the
71
4. SCC mix-design and properties
Figure 4.1.: Abrams cone and plate used in the Slump ﬂow test
Figure 4.2.: Slump ﬂow test carried out by a single operator, by using an Abrams cone
ﬁtted with a steel collar (photos taken at Stevin Laboratory, TUDelft, The
Netherlands)
top of the Abrams cone, as illustrated in Figure 4.2. The Slump-ﬂow test principle,
apparatus, procedure, result computation and precision are further described in Draft
prEN 12350-8 (CEN, 2007f).
V-Funnel test
The V-funnel test is used to assess the viscosity (and ﬁlling ability) of self-compacting
concrete (see Figure 4.3). This test result is the time taken for the concrete to ﬂow out
of a V-shaped funnel (tv, expressed to the nearest 0,5 s). The V-funnel test principle,
apparatus, procedure, result computation and precision are further described in Draft
prEN 12350-9 (CEN, 2007d).
72
4.2. Fresh properties and empirical tests
Figure 4.3.: V-Funnel test
L-Box test
The L-Box test is used to assess the passing ability of SCC through the gaps between
vertical, smooth reinforcing bars (12 mm) (see Figure 4.4). There are two variations:
the two-bar test (with a gap of 59 mm) and the three-bar test (with a gap of 41 mm) to
simulate situations of lower and higher reinforcement density, respectively. The L-box
can be made of steel or coated plywood. The ratio of the concrete heights in the front
and back parts of the box is taken as the test result (PA, expressed to the nearest 0,05).
The L-Box test principle, apparatus, procedure, result computation and precision are
further described in Draft prEN 12350-10 (CEN, 2007c).
Sieve segregation test
The sieve segregation resistance test is used to assess the resistance of SCC to seg-
regation. After sampling, the fresh concrete is allowed to stand for 15 min and any
separation of bleed water is noted. The top part of the sample is then poured into a
sieve with 5 mm square apertures (see Figure 4.5). After 2 min the weight of material
which has passed through the sieve is recorded. The test result is the segregation ratio
(SR, recorded to the nearest 1%), calculated as the proportion of the sample passing
through the sieve. The sieve segregation test principle, apparatus, procedure, result
computation and precision are further described in Draft prEN 12350-11 (CEN, 2007e)
.
73
4. SCC mix-design and properties
Figure 4.4.: L-Box test
Figure 4.5.: Sieve segregation test (photos taken at Stevin Laboratory, TUDelft, The
Netherlands)
74
4.2. Fresh properties and empirical tests
Figure 4.6.: J-Ring test
J-Ring test
Similarly, to the L-Box test, the J-ring test is used to assess the passing ability of SCC
through tight openings (see Figure 4.6). The bar spacings are the same as for the L-
Box (59 and 41 mm), and 12 and 16 smooth bars are used. The narrow bar spacing
simulates more congested reinforcement. The test result is given by the diﬀerence of
concrete heights inside the ring and just outside the ring, also called the J-ring blocking
step (BJ, recorded to the nearest millimetre). The J-Ring test principle, apparatus,
procedure, result computation and precision are further described in DRAFT prEN
12350-12 (CEN, 2007b).
Correlation between empirical test results and rheological parameters
The empirical tests described above provide an index of workability that may or may not
be related to more fundamental rheological parameters (for instance, the Bingham model
parameters). Within the EU “Testing SCC” research project, the best correlations with
the yield stress value were found with slump ﬂow value (SF) and the L-box blocking
ratio (PA) with R
2
equal to 0,76 and 0,73, respectively. The correlations of the yield
value with the other test results were with R
2
below 0,4. T500 (from the slump ﬂow
test) and the V-funnel time (tv) showed the best correlation with plastic viscosity, with
R
2
equal to 0,76 and about 0,6, respectively. The observed correlations of the plastic
viscosity with slump ﬂow and L-box blocking ratio were rather low (ACM Centre, 2005).
Furthermore, the empirical test results are related to each other to some degree, since
they measure similar properties. Some relation was found between tv and T500 results
and between PA and BJ results (Cussigh, 2007; ACM Centre, 2005). Table 4.1, indicates
the property(ies) primarily assessed by each test method but the response of each test
method may be aﬀected by other properties of concrete being tested. For example, large
V-funnel times can be originated by very viscous mixtures or blocking of aggregates near
75
4. SCC mix-design and properties
the exit aperture with segregating mixtures. Summing up, the three key SCC properties
(i.e. ﬁlling ability, passing ability and segregation resistance) are not independent (ACM
Centre, 2005).
4.2.3. Speciﬁcations for SCC
Speciﬁcation of fresh SCC properties (in terms of consistency classes or a target value)
is strongly dependent on the speciﬁc application and/or site requirements, such as, con-
ﬁnement conditions, placing equipment, placing methods and ﬁnishing method (ACM
Centre, 2005). Figure 4.7 exempliﬁes the classes to be speciﬁed in diﬀerent SCC applica-
tion types, based on BIBM et al. (2005); Walraven (2003) and Draft prEN 206-9 (CEN,
2007a). For all application types, a slump-ﬂow class (SFi) will normally be speciﬁed,
according to Figure 4.7 and Table 4.1. Where necessary, speciﬁc additional requirements
can be used to fully describe the performance of an SCC mix, by specifying a viscosity
class (VSi/VFi), a passing ability (PLi/PJi) class, a stability class (SRi) (according to
Figure 4.7 and Table 4.1) and/or other technical requirements (for example, consistence
retention time). A low viscosity SCC may be of interest where good surface ﬁnish is
required, but it can be more prone to bleeding and segregation. In contrast, a viscous
SCC tends to exhibit more thixotropy, which could be of interest to limit the lateral
stresses in the formwork or to improve the static segregation resistance. Regarding
passing ability, it is necessary to consider the smallest gap between reinforcement bars
through which SCC has to ﬂow to ﬁll the formwork, here called ﬂowing gap (fgap). The
cover thickness of the reinforcement bars is not to be taken into account in the ﬂow-
ing gap computation. Draft prEN 206-9, Annex L, recommends classes PL1/PJ1 and
PL2/PJ2 for typical housing and civil engineering structures, respectively, depending
on the ﬂowing gap (see Figure 4.7). If there is little (fgap>100 mm or thin slabs with
fgap>80 mm) or no reinforcement, there may be no need to specify passing ability as
a requirement. Stability becomes increasingly important for higher ﬂuidity and SCC of
lower viscosity. In Figure 4.7, SR1 and SR2 are indicated for horizontal and vertical
applications depending on the ﬂow distance (fdist) and ﬂowing gap, according to the
guidelines presented in Draft prEN 206-9, Annex L. For vertical applications, if the
maximum ﬂow distance (height) is higher than 5 m a SR lower than 10% is indicated
in Draft prEN 206-9 (CEN, 2007a).
In addition to those in EN 206-1 (2000), Draft prEN 206-9 (CEN, 2007a) speciﬁes
requirements for the constituent materials of SCC, the properties of fresh and hardened
SCC and their veriﬁcation, the limitations for SCC composition, the speciﬁcation of
SCC, the factory production control procedures and the conformity criteria. Because
some parts of ENV 13670 indicated the need for mechanical compaction, the reviewed
document Draft prEN 13670 (CEN, 2008) has included speciﬁc clauses for SCC.
76
4.3. Mix-design methods
Figure 4.7.: Consistency class’ speciﬁcation of SCC for most common applications,
adapted from (BIBM et al., 2005; Walraven, 2003) and Draft prEN 206-
9 (CEN, 2007a)
4.3. Mix-design methods
A considerable number of mix-design methods have been developed independently by
many academic institutions and construction industry companies (BIBM et al., 2005;
Koehler and Fowler, 2007). Still, there is no standard method for SCC mix design. The
methods vary widely in overall approach and in terms of complexity.
From early research on SCC in Japan, the Japanese proposed a SCC-designing method
(Okamura et al., 2000), which has been followed and further developed by several re-
searchers (Nunes et al., 2001; Takada et al., 1998). In this method, the coarse and
ﬁne aggregate contents are ﬁxed and the paste composition is adjusted, based on tests
carried out on the paste and mortar levels, to obtain a starting point for trial mixes on
concrete. This method was suggested in Europe by EFNARC (2002) and in the US by
the Precast/Prestressed Concrete Institute (Koehler and Fowler, 2007). The mixture
parameters and test methods involved in this method were used in the present work,
thus this mix-proportioning method is further described in Section 4.6.
Most methods consider SCC as a suspension of aggregates in paste. Thus, to proportion
SCC three factors must be deﬁned: the aggregates blend, paste volume, and paste
composition. Some examples of test methods following this approach are: the Excess
Paste Theory (Maeyama et al., 1998; Midorikawa et al., 2001; Oh et al., 1999), the
Swedish CBI Method and modiﬁcations (Billberg, 2002; Bui and Montgomery, 1999;
77
4. SCC mix-design and properties
Petersson and Billberg, 1999), the ACBM Paste Rheology Model (Saak, 2000), the
Densiﬁed Mixture Design Algorithm Method (Chang, 2004), the Particle-Matrix Model
(Smeplass and Mortsell, 2001), the ICAR Mixture Proportioning Procedure for SCC
(Koehler and Fowler, 2007) and the methodology suggested by Gomes (2002) for high
strength SCC. A common feature of these test methods is that the paste composition
is designed independently to the rest of the mixture. Nevertheless, the strategies used
to deﬁne the aggregates blend and to select the paste volume vary with each method.
Besides, each method uses a diﬀerent series of tests and has diﬀerent target values for
selecting the paste composition. In the ﬁnal stage, paste volume, paste composition,
and aggregate blend are combined for the preliminary trial concrete batch or batches.
Typically, a trial and error approach is followed in the ﬁnal stage which consists in
testing a ﬁrst trial batch, evaluating the results, and then adjusting the mixture propor-
tions, based on deducted relationships between the mixture parameters (Okamura et al.,
2000) and existing knowledge or recommendations (BIBM et al., 2005) and, ﬁnally, re-
testing the adjusted mixture. This procedure is repeated until the required properties
are achieved, which may involve carrying out a large and unpredictable number of trial
batches. Besides, this optimization technique may not lead to a general solution of the
problem. In contrast, Statistical Experimental Design is a more scientiﬁc and eﬃcient
approach for establishing an optimized mixture for a given constraint, while minimizing
the number of experimental data points (Nehdi and Summer, 2002). Models established
based on factorial design highlight not only the signiﬁcance of the experimental vari-
ables but also that of their interactions. These models are valid for a wide range of
mix proportioning and have a predictive capability for the responses of other points
located within the experimental domain. This design approach was followed by other
authors for many diﬀerent purposes, namely, to design and optimize mixtures, to com-
pare responses obtained from various test methods, to analyse the eﬀect of changes in
mixture parameters (to evaluate SCC mixture robustness) and to evaluate trade-oﬀs be-
tween key mixture parameters and constituent materials (for example, superplasticizer
and viscosity agent) (Bayramov et al., 2004; Khayat et al., 1999c,a, 2000; Nehdi and
Summer, 2002; Nunes et al., 2006; Sonebi et al., 2007, 2005; Yahia and Khayat, 2001b).
A more scientiﬁc approach is also possible with the Compressible Packing Model devel-
oped by de Larrard (1999) for high performance concretes, which has been applied to
SCC (Sedran and de Larrard, 1999; Sedran, 1999) and self-compacting ﬁbre reinforced
concrete (Grünewald, 2004). Concrete is seen as a suspension of solid particles in water.
Proportioning is based on a packing model, to predict the packing density of the solid
skeleton, which takes into account the packing process, the size distribution and shape of
the particles and the degree of ﬂocculation of ﬁner particles. Equations are available for
computing yield stress, plastic viscosity, a parameter representing ﬁlling/passing ability,
78
4.4. SCC mix-proportions
and a parameter representing segregation resistance (Sedran, 1999). Requirements for
hardened properties can also be included (de Larrard, 1999). Therefore, initial trial
proportions can be optimized numerically and must then be veriﬁed with laboratory
trial batches.
In the ﬁrst years of research on SCC at FEUP the Japanese SCC-designing method
was applied with some modiﬁcations (Nunes et al., 2001, 2004, 2005a). This approach
proceeds essentially by trial and error, using conventional single factor experiments re-
sulting in mixes that might not be optimal in terms of aggregate and paste contents.
Thus, within the present PhD research project, a more scientiﬁc mix-design method
was searched for dealing with constituent materials speciﬁc properties and resulting in
an optimized concrete mixture, for deﬁned performance requirements. From the mix-
design approaches described before, Experimental Factorial Design and the Compress-
ible Packing Model were found to be the most satisfactory. In the author’s opinion, the
Compressible Packing Model, although a very interesting and valuable approach, was
not selected in the present work because it presents, in practice, some barriers for general
use. In particular, it requires the use of the model expressions detailed in (de Larrard,
1999) and the calculation of yield stress and plastic viscosity based on empirical mea-
surements with the BTRHEOM, which is not available in most laboratories (it was not
available at FEUP) and typically leads to higher results compared to other rheometers
(Banﬁll et al., 2000). Conversely, the Experimental Factorial Design approach is a more
universal and versatile approach. It has been widely used in other industries and does
not require speciﬁc software. It is based on well-known statistical concepts and various
commercial software and bibliography are available to help on these analyses. An addi-
tional advantage of this approach is that the researcher has some freedom to deﬁne the
mixture parameters (it can be applied to paste, mortar or concrete) and the responses
to be analysed (eg rheological parameters, empirical fresh test results, hardened con-
crete properties, etc.). This mix-design approach was followed in the work presented in
Chapters 5 and 7 and is further described in Section 4.7.
4.4. SCC mix-proportions
The representation of an SCC mix as a suspension provides a consistent, fundamental
framework for discussing the inﬂuence of mixture proportions on the key fresh SCC
properties. As mentioned before, to proportion SCC the aggregates blend, paste volume
and paste composition must be deﬁned. The paste volume depends primarily on the
aggregates blend characteristics and the paste composition depends both on aggregates
blend characteristics and paste volume (Koehler and Fowler, 2007).
79
4. SCC mix-design and properties
4.4.1. Aggregates
For a given application, the aggregates content should be maximized (which minimizes
the paste volume) to reduce the cost of the material per m
3
and to improve hardened
properties, without impairing self-compactability. The main factors determining ag-
gregates content are: maximum size, particles size distribution, shape and angularity
(Koehler and Fowler, 2007). When having more than one aggregate source (ﬁne, in-
termediate or coarse) this must be considered. All of these factors will determine the
maximum packing density of aggregates blend or the voids content of compacted ag-
gregates. The selection of aggregates based on a minimum voids content criteria (or
maximum packing density) is not necessarily the best criteria in all cases (Koehler and
Fowler, 2007; de Larrard, 1999). For instance, an increase of maximum aggregate size
may improve grading and reduce voids content but may not be possible due to passing
ability and/or segregation resistance requirements. Thus, there is no universal optimal
grading for SCC (Koehler and Fowler, 2007; de Larrard, 1999). In general, equidimen-
sional, well-rounded aggregates are preferred, since they increase packing density and
reduce interparticles friction. But, aggregates of all shape and angularity can be ac-
commodated in SCC by increasing the paste volume (Koehler and Fowler, 2007; BIBM
et al., 2005).
4.4.2. Paste volume
At concrete level, SCC can be seen as a suspension of aggregates in paste (see Fig-
ure 4.8), where the paste volume is set to be greater than the volume of voids between
the compacted aggregates so that a thin layer of paste forms around the aggregate par-
ticles (excess paste) (Koehler and Fowler, 2007; Grünewald and Walraven, 2007). This
excess paste increases ﬂuidity and reduces aggregate inter-particle friction. If this layer
is slightly too thin then frictional forces develop and destroy the self-compacting prop-
erties, regardless of the composition of the paste. If, however, it is slightly too thick,
segregation of aggregates may occur. Thus, the required paste volume is determined by
the packing density and surface area of aggregate to provide adequate spacing between
aggregate particles (Koehler and Fowler, 2007; de Larrard, 1999). The minimum excess
paste needed for concrete to ﬂow can range from 8% for equidimensional, well-rounded
aggregates to 16% for poorly shaped, angular aggregates (Koehler and Fowler, 2007).
Additional paste can be used to increase the robustness of SCC mixtures (Koehler and
Fowler, 2007), as it is demonstrated in Chapter 7.
80
4.4. SCC mix-proportions
Figure 4.8.: Schematic representation of concrete as a suspension of aggregates in paste,
taken from (Koehler and Fowler, 2007)
4.4.3. Paste composition
Once the paste volume is suﬃcient for a given aggregate blend, concrete workability can
be further enhanced by adjusting the paste composition. At paste level, SCC paste can
be seen as a suspension of powder particles in water (Grünewald and Walraven, 2007;
Midorikawa et al., 2001). The optimum paste characteristics depend on the aggregates
skeleton. A decrease of the excess paste to ﬁll the aggregate skeleton can be compen-
sated by an increase of the thickness of water around the powder grains and vice-versa
(Grünewald and Walraven, 2007; Midorikawa et al., 2001). The rheological properties
of the paste are adjusted and balanced by careful selection and proportioning of the
cement and additions, by limiting the water/powder ratio and then by adding a super-
plasticiser and (optionally) a viscosity-modifying admixture (VA). Besides controlling
workability, the paste composition has a large inﬂuence on early-age properties and long
term hardened properties, including durability. Thus, in order to control temperature
rise and thermal shrinkage cracking, as well as strength and durability, the ﬁne powder
content may contain a signiﬁcant proportion of type I or II additions to keep the cement
content at an acceptable level. Tests can be conducted on paste or mortar to evaluate
the relative eﬀects of various constituents; however, the ﬁnal paste composition should
be veriﬁed in concrete.
Depending on paste composition, SCC mixes may be classiﬁed into one of three types
(Grünewald and Walraven, 2004):
• powder-type SCC, characterized by low water powder ratios and high superplas-
ticizer dosages;
• VA-type SCC, characterized by high water powder ratios and a signiﬁcant dosage
of a VA;
• combination-type SCC, which is intermediate to the previous ones, i.e. moderately
81
4. SCC mix-design and properties
low water powder ratios and small dosages of VA.
4.4.4. Typical values for SCC mix-proportions
Based on an analysis of 68 case studies from diﬀerent countries, published between 1993
and 2003, performed by Domone (2006), typical values of SCC mix-proportions are
presented in Figure 4.9, by using a box-plot
1
representation. In these 68 case studies 50%
of the mixtures included VA, thus the results presented in Figure 4.9 are representative
of all types of SCC. In most of the case studies maximum coarse aggregate sizes of
16 to 20 mm were used (Domone, 2006). Nearly all mixtures included some type of
additions, with limestone powder being the most common one (Domone, 2006). The
range of values presented in Figure 4.9 are, in general, in line with those provided by
BIBM et al. (2005): a coarse aggregate content of 27 to 36 % (in volume); a paste
content of 30 to 38% (in volume); powder content of 380 to 600 (kg/m
3
). BIBM et al.
(2005) added the following typical mix-proportion values: water powder ratio of 0,85 to
1,10 (in volume); a water content of 150 to 210 (kg/m
3
); and a ﬁne aggregate content
of 48 to 55% of total aggregate weight. Observing Figure 4.9, it can be noticed that
the size of the box and whiskers are smaller in the cases of V
g
, V
paste
and V
s
/V
m
when
compared to w
w
/w
p
and w
p
. This indicates that the mix-proportions related to the
aggregate skeleton are more critical to attain self-compactability and there is a more
wide range of solutions concerning paste composition (Domone, 2006).
By analysing the subset of mixes incorporating VA (34 case studies) and the subset of
mixes without VA (34 case studies), some clear diﬀerences can be observed in terms of
powder contents and water/powder ratio (see Figure 4.10). Since the diﬀerences between
the median values are relatively small it can be concluded that most mixtures including
VA are of the combination–type. These include only a small amount of VA, which is
often added to reduce the sensitivity of the mix to variations of grading or moisture
content of the aggregate (Domone, 2006). With further development of admixtures and
the widespread use of VAs one can expect larger diﬀerences between mixtures with and
without VA (Domone, 2006; Grünewald and Walraven, 2005).
Compared to conventional workability concrete, SCC will generally contain lower coarse
aggregate contents, larger paste contents, lower water/powder ratios, higher superplasti-
cizer dosages and sometimes a VA. Nevertheless, increasing the paste volume is not nec-
essarily associated with increasing the cement or cementitious materials content (Con-
crete Society, 2005).
1
The line within the box marks the median and the boundaries of the box closest and farthest to zero
indicate the 25
th
percentile and the 75
th
percentile, respectively. The whiskers above and below the
box indicate the 90
th
and 10
th
percentiles. In addition, the extreme and outlier points are plotted
as empty circles and stars, respectively.
82
4.4. SCC mix-proportions
Figure 4.9.: Box-plots of SCC mix-proportions of 68 case studies (Domone, 2006)
(a) (b)
Figure 4.10.: Box-plots of (a) w
w
/w
p
and (b) w
p
of SCC mixtures not including VA (34
case studies) and including VA (34 case studies) (Domone, 2006)
83
4. SCC mix-design and properties
4.5. Hardened properties
In spite of SCC being made basically of the same constituent materials as conventional
concrete, signiﬁcant diﬀerences exist regarding their mix-proportions, as shown in the
previous section, and also in the placing and compaction processes. Therefore, it is
pertinent to discuss the possible diﬀerences between hardened properties of SCC and
conventional concrete and the applicability of currently existing design rules. From the
structural designers’ point of view it is the hardened properties that are of paramount
interest.
In the following sections, a short review of the inﬂuence of SCC mix proportions on
the hardened properties is given, with reference to hardened properties data obtained
during mix development and full-scale test studies carried out under BACPOR and
POCI/ECM/61649/2004 research projects.
4.5.1. SCC .vs. conventional concrete
Considering hardened SCC and conventional vibrated concrete of similar strength, it can
be assumed that properties are comparable and any diﬀerences lie in the scattering range
for conventional concrete (BIBM et al., 2005; Walraven, 2005). Often, for SCC, similar or
higher compressive strength, lower modulus of elasticity, higher splitting tensile strength,
higher shrinkage and better bond to the reinforcement are reported (BIBM et al., 2005;
Walraven, 2005). Similar trends were found when comparing properties of hardened
SCC cast during full-scale tests in a precast factory and similar conventional concrete
currently used in the same factory, within BACPOR research project (see paper included
in Appendix A for further details).
Due to increased content and variety of ﬁne materials (cement, limestone ﬁller, ﬂy
ash, etc) and dispersing eﬀect of superplasticizers an improved microstructure can be
obtained, related to higher packing density of paste and reduced size and porosity of
the interfacial transition zone (ITZ) (Klug and Holschemacher, 2003). This can explain
improvements on compressive and tensile strengths of SCC (Klug and Holschemacher,
2003). In addition, SCC should exhibit slightly higher compressive strength as a result
of the absence of vibration, which improves the bond between aggregate and paste
(BIBM et al., 2005). It was found that limestone powders can also accelerate hydration,
resulting in increased compressive strength up to 28 days (Domone, 2007; Klug and
Holschemacher, 2003; Zhu and Gibbs, 2005). Limestone ﬁller can react with C
3
A forming
carboaluminate and with C
3
S accelerating the hydration of C
3
S and modifying the
Ca/Si ratio of C-S-H (Pera et al., 1999). Research has also shown that SO
4
ions in
ettringite are replaced with carbonate ions when ground limestone ﬁller is present (Pera
84
4.5. Hardened properties
et al., 1999). Based on analysis and comparison of data from more than 70 case studies
(Domone, 2007), the ratio of cylinder to cube strength varies from about 0,8 to 1,0 at
strengths of about 30 to 90 MPa, respectively, which is generally higher than values used
for conventional concrete. Reduction in modulus of elasticity of SCC could be expected
due to lower content of coarse stiﬀ aggregates. Elastic modulus of SCC can be up to
40% lower than that of conventional concrete at low compressive strength (20 MPa),
but the diﬀerence reduces to less than 5% at high strengths (90-100 MPa) (Domone,
2007).
Based on database results from around the world (Klug and Holschemacher, 2003), dry-
ing shrinkage of SCC is 10 to 50% higher than that of conventional concrete (predicted
by the CEB-FIP model code). Drying shrinkage of SCC may be higher than in conven-
tionally placed concrete primarily due to higher paste volumes. The more highly reﬁned
pore structure of SCC (especially when reducing water-cementitious material ratio below
0,40, adding silica fume, increasing the cement ﬁneness, etc) may also increase the risk
of autogeneous shrinkage (Koehler and Fowler, 2007). Higher cementitious materials
content and lower water-cementitious ratios can increase the susceptibility to thermal
volume changes. The higher volume changes sometimes associated with SCC may not
necessarily result in increased cracking risk due to higher tensile strength, lower modulus
of elasticity, and higher creep.
Bond behaviour of reinforcement in concrete is strongly inﬂuenced by quality of the ITZ
between the paste and embedded reinforcement (Valcuende and Parra). Furthermore,
the position and orientation of a bar in the formwork has also a signiﬁcant eﬀect on its
bond strength (the so-called top-bar eﬀect). The top-bar eﬀect arises from reduction
of bond eﬃciency by formation of voids under horizontal bars which are perpendicular
to the casting direction of concrete. Rising bleed water can be trapped under bars
and plastic settlement of concrete can leave air voids under the bars which will impair
the quality of concrete-steel interface in these zones. In spite of the limited number of
studies on bond strength in SCC, in general it was found that bond strength is greater
in SCC than conventional concrete (Valcuende and Parra). The diﬀerences between
both types of concrete tend to even out as the strength of concrete increases (Valcuende
and Parra). Consequently, Valcuende and Parra proposed a reduction of the anchorage
length of reinforcement, dependent on concrete compressive strength, for the speciﬁc
case of high viscosity powder-type SCC. SCC also tends to exhibit less variation of
bond strength in height. In this respect, Valcuende and Parra also proposed a change to
the factor that takes into account the top-bar eﬀect for calculating the anchorage length
of reinforcement. The improved bond behaviour of reinforcement in SCC is justiﬁed by
SCC’s ﬁlling ability, allowing the concrete to cover the reinforcement more eﬀectively,
and SCC’s stability, minimizing bleeding, segregation and surface settlement (Valcuende
85
4. SCC mix-design and properties
and Parra; Domone, 2007).
Transport properties of the near-surface concrete play a major role in durability of re-
inforced concrete (Neville, 1995; Sousa Coutinho, 2005). Usually, the more resistant
concrete is to the ingress of aggressive agents (pure water or carrying ions, oxygen and
carbon dioxide) the more durable it will be (Neville, 1995). The transport properties of
concrete depend primarily on the paste volume, pore structure of the paste and ITZ be-
tween paste and aggregate particles (Zhu et al., 2001). Although SCC has higher paste
volume, the pore structure of the bulk paste and the ITZ are often improved due to the
low water-cementitious materials ratios and the use of additions, but not all additions
have the same eﬀect. Zhu et al. (2001) found that chloride diﬀusivity was very much
dependent on the type of addition used in concrete. In summary, the permeability and
diﬀusivity of SCC may be higher or lower than conventionally placed concrete depend-
ing on the mixture composition. So far, limited information is available in literature
concerning all relevant durability issues, like carbonation, chloride penetration, frost
resistance, sulfate attack, thaumasite formation and ﬁre resistance. Concerning this
subject, work is currently being carried out under RILEM Technical Committee TC
205-DSC: Durability of self-compacting concrete (Rilem Technical Committee, 2008).
Variation of in-situ properties within the structural elements and the relationship be-
tween the in-situ properties to those of standard specimens, made from samples of con-
crete taken at casting, are very important for both design and structural performance.
In this respect, the use of SCC can eliminate defects due to vibration and assure a
more uniform distribution of properties. Furthermore, the high segregation resistance
of SCC can lead to enhanced homogeneity. Indeed, in one of the full-scale tests carried
out within BACPOR research project, it was found that concrete strength measured
at diﬀerent locations of the box-culvert element disperses less than in a similar element
with conventional concrete (see paper included in Appendix A for further details). Com-
paction resulting from external vibration is uneven depending on the distance to the
vibration sources. To sum up, SCC provides potential for a superior level of homogene-
ity and durability for the structure. Nevertheless, mixes must have adequate ﬁlling and
passing ability for the speciﬁc application and any tendency to segregation can have
signiﬁcant detrimental eﬀects, as observed during “SCUT-IP5” full-scale tests carried
out within BACPOR research project (see paper included in Appendix A for further
details). In this case study, a signiﬁcant reduction of strength and density of concrete
cores, taken from high panels (height=5 m; length=1 m; thickness=0,20 m) casted with
segregating SCC, was observed between the middle and the top of the panels (Nunes
et al., 2005b).
86
4.5. Hardened properties
Table 4.2.: Mix proportions and test results of SCC mixtures developed within
POCI/ECM/61649/2004 research project
Mix A Mix B Mix C
CEM II/A-L 42,5 R CEM I 52,5R CEM IV/B(V) 32,5 N
Constituent materials (kg/m
3
)
cement 331 332 553
limestone ﬁller 269 270 0
water 156 156 162
superplasticizer (V3000) 10,19
a
11,45
b
12,21
sand 1 (natural) 397
sand 2 (natural) 369
coarse aggregate 827
Fresh test results
Dﬂow (mm) 708 685 785
T50 (s) 2,4 2,4 2,4
Tfunnel (s) 13,7 15,7 10,4
H2/H1 > 0,80 > 0,80 > 0,80
∆Dﬂow/∆Ww
c
(mm/kg/m
3
) 6,9 9,6 8,5
Estimated cost (€/m
3
) 55 60 65
a
the equivalent to 0,624 kg/m
3
was discounted to account for the mixer eﬀect;
b
the equivalent to 0,700 kg/m
3
was discounted to account for the mixer eﬀect;
c
sensitivity of mixtures to changes in water content is taken here as an indicator of mixture robustness.
4.5.2. Tailor-made SCC
For a given application, SCC can often be designed to have equal or better hardened
properties than conventional concrete by making use of the trade-oﬀs associated with
mixture parameters and type of materials. Although low water-powder ratios are usually
dictated by workability requirements, the water-cement ratios can be varied much more
widely depending on the quantities of ﬁllers used, as it will be clariﬁed in Chapter 5. The
rate of development and ultimate values of strength, the elastic modulus, dimensional
stability and transport properties depend on the amount and activity of these ﬁllers
(including those incorporated in cement). This was demonstrated in a study carried out
within POCI/ECM/61649/2004 research project. As it is shown in Tables 4.2 and 4.3,
diﬀerent strategies for mixture proportioning may lead to SCC materials that have
similar fresh state properties but have diﬀerent behaviour when considering cost, sensi-
tivity to water content variations, mechanical performance, early-age cracking risk and
durability. In these three SCC mixtures, the aggregate skeleton was maintained and
only paste composition was altered. A further detailed characterization of these SCC
mixtures is given in Appendix B.
87
4. SCC mix-design and properties
Table 4.3.: Hardening and hardened properties of SCC mixtures developed within
POCI/ECM/61649/2004 research project
Mix A Mix B Mix C
Mechanical properties at 10 days
f
cm,cube
(MPa) 49, 3 58, 6 48, 6
f
cm,cylinder
(MPa) 43, 4 51, 2 42, 2
f
ctm
(MPa) 3, 6 4, 1 3, 2
E
cm
(GPa) 41, 4 42, 5 38, 2
Mechanical properties at 28 days
f
cm,cube
(MPa) 50, 6 62, 7 58, 4
f
cm,cylinder
(MPa) 47, 0 53, 7 51, 4
f
ctm
(MPa) 3, 8 4, 1 3, 5
E
cm
(GPa) 42, 0 44, 0 40, 8
Temperature evolution
Time to start increase 4 h20 min 4 h30 min 7 h10 min
Time to reach the peak temperature 12 h00 min 11 h35 min 15 h20 min
∆Tmáx. (
◦
C) 6, 2 7, 9 7, 9
Drying shrinkage deformations (mm/mm)
7 days after demoulding 43, 7 ×10
−6
72, 3 ×10
−6
86, 4 ×10
−6
28 days after demoulding 125, 7 ×10
−6
183, 0 ×10
−6
224, 8 ×10
−6
205 days after demoulding 238, 3 ×10
−6
297, 6 ×10
−6
349, 5 ×10
−6
Creep strain deformations (mm/mm)
10 days after loading 194, 8 ×10
−6
183, 5 ×10
−6
167, 9 ×10
−6
100 days after loading 379, 4 ×10
−6
342, 9 ×10
−6
281, 6 ×10
−6
170 days after loading 424, 1 ×10
−6
374, 1 ×10
−6
321, 2 ×10
−6
Transport properties
D
ns
(m
2
/s) 21, 7 ×10
−12
18, 8 ×10
−12
7, 4 ×10
−12
Sorptivity (g/(m
2
/min
0,5
)) 73, 29 49, 53 45, 71
88
4.6. Japanese SCC-designing method
By comparing results in Tables 4.2 and 4.3, the most positive aspects of Mix A are lower
cost, less sensitivity to water content changes, lower heat of hydration release and lower
shrinkage deformations. The most positive aspects of Mix B are faster development of
early mechanical properties, higher ﬁnal strengths (28 days) and modulus of elasticity.
Considering Mix C, the most positive aspects are that no additional silo is required for
an extra addition, improved transport properties and lower creep deformations. Fur-
thermore, the diﬀerences in strength observed at 28 days between Mix C and mixes A
and B should be less pronounced at later ages.
4.5.3. Concluding remarks
In the view of the foregoing, SCC is seen as a concrete family that diﬀers only when
observed at fresh state. Hardened properties should be evaluated in the same manner as
for conventionally placed concrete. The relationships between hardened properties and
materials and mixture proportions for conventional concrete generally apply to SCC.
Overall, analysis of data from test programmes carried out in the past few years has led
to conclude that diﬀerences between SCC and conventional concrete of similar strength
are small and covered by the safe assumptions in the tables and the formulae provided in
the design codes (Domone, 2007; Klug and Holschemacher, 2003). No further revisions
of existing standards were found to be necessary regarding SCC hardened properties
(Eurocode 2)(Cussigh, 2007).
SCC is a wide family of mixes and there is no unique mix for a given application or set
of performance requirements (Domone, 2006). Therefore, the need for a more scientiﬁc
and eﬃcient approach is emphasized in order to establish tailor-made mixtures that
can achieve the desired properties such as ﬁlling ability, passing ability, segregation
resistance, but also hardened properties, economy, and robustness.
4.6. Japanese SCC-designing method
4.6.1. Mix-proportioning system
According to the Japanese SCC-designing method, suggested by Okamura et al. (2000),
the mix proportions are determined as follows:
1. air content is set at 2%, unless air entrainment is required when freeze-thaw resis-
tant concrete is to be designed;
2. the coarse aggregate content in concrete, V
g
, is limited to 50% of the dry rodded
content (V
g,lim
) excluding air volume;
89
4. SCC mix-design and properties
3. the ﬁne aggregate volume, V
s
, corresponds to 40% of the mortar volume (V
m
);
4. the water to powder volume ratio, V
w
/V
p
, is determined on the basis of paste and
mortar tests;
5. the dosage of superplasticizer Sp/p (% of powder weight) is adjusted by a test on
fresh concrete, to ensure self-compactability;
6. ﬁnally, tests are performed on trial batches of concrete to ﬁnalize the mixture
proportions.
There are also some important conditions on applying this method (Takada et al., 1998):
• the maximum aggregate size is 20 mm;
• the border size between ﬁne and coarse aggregates is 5 mm;
• the particles ﬁner than 0, 09 mm are not considered as aggregate but as powder;
• Japanese moderate heat Portland cement is used as a standard powder material.
4.6.2. Paste and mortar tests
Physical properties of each powder materials composition can be estimated by a small set
of ﬂow tests on pastes (by using the mini-slump ﬂow cone presented in Figure 4.11 (a))
with diﬀerent water to powder volume ratios (for example, 1,1, 1,2, 1,3, and 1,4 by vol-
ume). Okamura et al. (2000) found that, for a paste made with any particular powder
or powders composition, the relative ﬂow area (G
p
) given by
G
p
= (d −100)
2
−1 with d = (d1 + d2)/2 (4.1)
and the water powder ratio by volume (V
w
/V
p
) are linearly related. With the linear
relation obtained by regression analysis of data, the water retaining ratio (β
p
) and the
deformation factor (E
p
) can then be determined. The β
p
is the water to powder volume
ratio for which the deformation of the paste is zero; it can be thought as comprising the
water adsorbed on the powder surface together with that required to ﬁll the voids among
the powder particles (Takada, et al., 1998). E
p
is a measure of the sensitivity of the
paste ﬂow diameter to increasing water content (Domone and Hsi-Wen, 1997). Water
demand of diﬀerent powder types and powder compositions is compared in Table 4.4.
Originally this test was carried out in pastes not including superplasticizer, Domone
and Hsi-Wen (1997) showed that the addition of superplasticizer reduces both β
p
and
E
p
values.
90
4.6. Japanese SCC-designing method
(a) (b)
Figure 4.11.: (a) Mini-slump ﬂow cone and (b) mini-V-funnel used in paste and mortar
tests in the Japanese SCC-designing method (Okamura et al., 2000)
Table 4.4.: β
p
and E
p
results for single powders and mixtures of powders
Powder/mixture
a
β
p
E
p
Powder/mixture
b
β
p
E
p
pc 1,08 0,061 100% pc 1,04 0,048
pfa 0,59 0,024 85% pc+15% lsp 0,98 0,052
ggbs 1,10 0,046 70% pc+30% lsp 0,93 0,053
lsp 0,77 0,037 60% pc+40% lsp 0,91 0,061
pc+1% Sp 0,86 0,034 60% pc+40% pfa 0,82 0,041
pc: Portland cement; pfa: pulverized ﬂy ash; ggbs: ground granulated blast furnace slag;
lsp: limestone powder; Sp: SNF based superplasticizer;
a
(Domone and Hsi-Wen, 1997)
;
b
(Nunes et al., 2004)
91
4. SCC mix-design and properties
Mortar tests using the ﬂow cone and the V-funnel (Figure 4.11) are suggested to evaluate
interaction between ﬁne materials, superplasticizer and ﬁne aggregate particles (Oka-
mura et al., 2000). These tests give a measure of deformability and viscosity through
the calculation of the indexes G
m
and R
m
, respectively. The relative ﬂow area (G
m
) can
be obtained from the measured ﬂow diameters (d1 and d2) on the mortar ﬂow test and
the relative funnel speed (G
m
) can be calculated from the measured time for mortar to
ﬂow out through the V-funnel (t), as follows:
G
m
= (d/100)
2
−1 (4.2)
R
m
= 10/t (4.3)
Larger G
m
values indicate higher deformability and smaller R
m
values indicate higher
viscosity. It was found that the adequate value of water/powder ratio (V
w
/V
p
) could
be reached when G
m
= 5, 0 and R
m
= 1, 0, simultaneously, which corresponds to a
slump ﬂow diameter of 250 mm and a V-funnel time of 10 s (Okamura et al., 2000). To
facilitate the search for the values of water/powder ratio and superplasticizer dosage to
achieve appropriate deformability and viscosity, relationships between Sp/p and G
m
/R
m
and between V
w
/V
p
and R
m
/G
0,4
m
were suggested (Okamura et al., 2000), reducing the
number of trial mixes.
4.6.3. Concrete tests
According to the type of mixer, the dosage of superplasticizer Sp/p has to be adjusted
with tests on fresh concrete, after mixing. Once initial mix proportions have been
deﬁned, self-compactability has to be tested by the Box (or U-box), slump ﬂow and V-
funnel tests (see paragraph 4.2.2) (Okamura et al., 2000). The Box test is recommended
to assess passing ability. This test result is the ﬁnal concrete height after passing through
parallel bars (see Figure 4.12). As an alternative to this test the European “Testing
SCC” project group recommended the L-box or J-ring tests for standardisation. A
properly designed SCC should result in a slump-ﬂow of 650 ±50 mm and V-funnel time
of 10 to 20 s (Takada et al., 1998).
4.6.4. Modiﬁcations to this method
The mix-proportioning system described above was recommended for robust, safe mixes,
but subsequent developments have shown this to be conservative. During early inves-
tigation on SCC carried out in The Netherlands, the coarse aggregate content was
92
4.6. Japanese SCC-designing method
Figure 4.12.: Box test
successfully increased up to 60% of the dry rodded content, when using river aggregates
and a maximum aggregate size of 16 mm (Takada et al., 1998). At this aggregate con-
tent, the required paste content was signiﬁcantly lowered and therefore a more eﬃcient
mix was obtained.
Based on the experience acquired with the use of SCC in Europe until 2002, in the
“European Guidelines and Speciﬁcations for Self-Compacting Concrete” published by
EFNARC (2002) the Japanese SCC designing method was recommended with a number
of modiﬁcations aimed at producing more eﬃcient mixes, which are applicable to a wide
range of materials used in Europe and to existing standards. The following modiﬁcations
were introduced to the original Japanese SCC designing method:
• coarse aggregate content (deﬁned as all particles larger than 4 mm) should be
between 50 and 60% of the dry rodded content, depending on maximum aggregate
size and shape of aggregates;
• ﬁne aggregate content (deﬁned as all particles larger than 0,125 mm and smaller
than 4 mm) should be between 40–50 % of the mortar volume;
• a target mini-slump ﬂow of 240 to 260 mm and a mini-V-funnel time of 7-11 s of
mortar should be attained.
4.6.5. Universal mixtures formulation
Although the Japanese SCC-designing method was not followed in the present PhD
research project, the recommended mixture variables and test methods (mortar tests)
were found to be adequate to characterize SCC mixtures, at the mortar and concrete
levels (see Chapters 5, 6 and 7).
93
4. SCC mix-design and properties
SCC mix proportions can be established based on the following mixture variables: wa-
ter to powder volume ratio (V
w
/V
p
); ﬁller to cement weight ratio (w
f
/w
c
); superplas-
ticizer to powder weight ratio (Sp/p); sand to mortar volume (V
s
/V
m
); solid volume
(V
ap
= V
g
/V
g,lim
), as suggested by Okamura et al. (2000). An additional variable must
be considered when ﬁne aggregate is a combination of two sands. In this work, weight
ratio (s
1
/s) sand 1 to total sand was used. When proportioning SCC, the volumetric
composition of the mix is considered ﬁrst, with subsequent conversion to proportions
by weight, due to the need to overﬁll the voids between the aggregate skeleton (Con-
crete Society, 2005). The volumetric composition of concrete, by cubic meter, is given by
V
s
+ V
g
+ V
p
+ V
w
+ V
a
= 1, 0 m
3
(4.4)
From the absolute values of the mixture variables the paste/mortar/concrete mix pro-
portions can be obtained using the following universal formulation:
First, for a given value of (V
ap
= V
g
/V
g,lim
) and a deﬁned air content (V
a
), the coarse
aggregate volume can be obtained from
V
g
=
V
g
V
g,lim
×V
g,lim
(1 −V
a
) (4.5)
and from equation 4.4 and for a given value of (V
s
/V
m
), the mortar and sand volumes
can be deﬁned as
V
m
= 1 −V
a
−V
g
(4.6)
V
s
=
V
s
V
m
×V
m
(4.7)
Finally, for a given value of (V
w
/V
p
), the powder volume is obtained from
V
p
=
V
m
−V
s
1 +
V
w
V
p
(4.8)
From the (V
w
/V
p
) and (w/c) values the ﬁller to cement weight ratio can be determined
as follows
w
f
/w
c
=

¸

¸
w/c
ρ
w
×
V
w
V
p
¸

−
1
ρ
c
¸

×ρ
f
(4.9)
where ρ
w
, ρ
c
and ρ
f
represent the speciﬁc gravity of water, cement and limestone ﬁller
(or any other addition), respectively. After determining the V
p
and w
f
/w
c
the weight
values of cement, limestone ﬁller and water can be obtained as follows
94
4.6. Japanese SCC-designing method
w
c
=
V
p
1
ρ
c
+
w
f
/w
c
ρ
f
(4.10)
w
f
= (w
f
/w
c
) ×w
c
(4.11)
w
w
=
V
w
V
p
×V
p
×ρ
w
(4.12)
From the superplasticizer dosage (Sp/p) and the weight values of cement and ﬁller the
liquid weight of superplasticizer is given by
w
Sp
=
Sp
p
×(w
c
+ w
f
) (4.13)
Since aggregates were often made of a mixture of two sands and gravel, the dry aggre-
gate contents can be obtained as follows
w
gd
= V
g
×ρ
g
(4.14)
w
sd
=
V
s
s
1
/s
ρ
sd1
+
(1−s
1
/s)
ρ
sd2
(4.15)
w
sd1
= (s
1
/s) ×w
s
; w
sd2
= (1 −s
1
/s) ×w
s
(4.16)
where ρ
g
, ρ
sd1
and ρ
sd2
represent the speciﬁc gravity of coarse aggregate, sand 1 and
sand 2, respectively. The water added to the mixture has to be corrected by subtracting
the water content of the superplasticizer, adding the water needed for saturating the
aggregates (when they are in a dry state) or subtracting the actual humidity of aggre-
gates as follows
w
wc
= w
w
−w
sp
×(1 −γ
Sp
) +
¸
i
(w
sdi
×(A
si
−H
si
)) + w
gd
×(A
g
−H
g
) (4.17)
where γ
Sp
, A and H represent the solid content of superplasticizer (%), the absorption
coeﬃcient of aggregates (%) and the humidity of aggregates (%), respectively.
When the aggregates are humid, the weights of the aggregates should also be corrected
as:
w
sdic
= w
sdi
×(1 + H
si
) (4.18)
95
4. SCC mix-design and properties
w
gdc
= w
gd
×(1 + H
g
) (4.19)
As it was mentioned before this is a universal formulation for concrete, mortar and paste.
The particular case of mortar is obtained when V
ap
equals zero; and the particular case
of paste is obtained when both V
ap
and V
s
/V
m
equal zero. This formulation was used to
obtain the mix-proportions of all mixtures presented in Chapters 5, 6 and 7.
4.7. Statistical design approach
4.7.1. Introduction
In the concrete mix-design process, experimentation is always present to a larger or
shorter extent, even with the more sophisticated and scientiﬁc mix-design methods.
Statistical design approach refers to the process of planning the experiment so that ap-
propriate data that can be analysed by statistical methods will be collected, resulting in
sound conclusions and with a minimum number of experiments. Statistical design ap-
proach oﬀers a valid basis for developing an empirical model that is an equation derived
from data that expresses the relationship between the response and the important de-
sign factors. This empirical model can then be manipulated mathematically for various
purposes (Montgomery, 2001).
In general, after formulating the problem, the statistical design approach involves the
following procedure steps (Montgomery, 2001):
1. choice of factors (mixture variables), levels and ranges;
2. selection of the response variable(s);
3. choice of the experimental design;
4. performing the experiments;
5. statistical analysis of the data (ﬁtting a model);
6. other computations with response models and conclusions.
The options made in each of these steps for the studies presented in Chapters 5 and 7,
are discussed in the next sections.
4.7.2. Experimental design, design factors and response variables
Since some knowledge exists of both the materials to be used and SCC mix-proportions
(see Section 4.4) the region to be investigated by the experimenter is relatively close
96
4.7. Statistical design approach
(a)
(b)
Figure 4.13.: Central composite designs for (a) k = 2 and (b) k = 3 (Montgomery, 2001)
to the optimum. In situations like this, a second order model is usually required to
approximate the response because of curvature in the true response surface.
The most commonly used design for ﬁtting a second order model is the Central Compos-
ite Design (CCD). The CCD is widely used in practice because it is relatively eﬃcient
with respect to the number of runs required. In general, a CCD consists of a 2
k
factorial
with n
F
runs, 2k axial runs and n
c
centre runs (Montgomery, 2001). Designs for k = 2
and k = 3 are shown in Figure 4.13.
The 2
k
factorial part of the design is necessary to study the joint eﬀect of the factors
on a response. The eﬀect of each of the k factors is evaluated at only two levels, the
“high” and “low” levels of the factor coded +1 and -1, respectively (Figure 4.13). A
complete replicate of such a design requires n
F
= 2
k
runs. Unfortunately, with a 2
k
factorial design one cannot estimate all the unknown parameters (the β

s) in a second
order model (see paragraph 4.7.4). A simple and highly eﬀective solution to this prob-
lem is to increase the 2
k
design with axial runs. Because the purpose of the model is
to provide predictions of the response in the region of interest, it is important to have
equal precision of estimation in all directions or a constant variance of the predicted
response at points equally distant from the design centre. A design with this property
is called Rotatable (Montgomery, 2001). The CCD may be made rotatable by proper
choice of the axial spacing (α). The value of α for rotability depends on the number of
points in the factorial part of the design (n
F
) (Montgomery, 2001), being given by
α = (n
F
)
1/4
(4.20)
Replicate runs at the centre of the design (n
c
runs) are added to give an estimate of
the experimental error. Generally, 3 to 5 centre runs are recommended (Montgomery,
2001). The reason for adding replicate runs at the design centre is that center points
97
4. SCC mix-design and properties
only aﬀect the estimate of the model constant or the global response average.
There are many other designs that can be useful in practice, one of them is a small
composite design, consisting of a fractional factorial design plus the usual axial and
centre runs (Montgomery, 2001). As the number of factors in a 2
k
factorial design
increases, the number of runs required for a complete replicate of the design rapidly
outgrows the resources (time and/or materials) of most experimenters (Figure 4.14).
When k is large, if the experimenter can reasonably assure that certain high-order
interactions are negligible, then a fractional factorial design 2
(k−p)
involving fewer than
the complete set of 2
k
runs can be used to obtain information on the main eﬀects and
low-order interactions. The concept of design Resolution (indicated by a Roman numeral
subscript in Figure 4.14) is a useful way to catalog fractional factorial designs according
to the alias patterns they produce (Montgomery, 2001). In fractional factorial design
two or more factors are called aliases when it is impossible to distinguish their eﬀects.
In Resolution III designs no main eﬀects are aliased with any other main eﬀects, but
they are aliased with two-factor interactions and two-factor interactions may be aliased
with each other. In Resolution IV designs no main eﬀects are aliased with any other
main eﬀects or any other two-factor interaction, but two-factor interactions are aliased
with each other. In Resolution V designs no main eﬀect or two-factor interaction is
aliased with any other main eﬀect or two-factor interactions, but two-factor interactions
are aliased with three-factor interactions. One should look for the highest possible
resolution because it requires less restrictive assumptions regarding which interactions
are negligible to obtain a unique interpretation of the data (Montgomery, 2001).
In many situations, the regular fractions presented in Figure 4.14 contain considerably
more runs than are necessary to estimate the p = 1 +k +k(k −1)/2 parameters in the
model containing up to two-factor interactions. For example, the smallest resolution
V fraction with k=6 uses 32 runs (Figure 4.14) to estimate the 22 parameters in the
model. Therefore, many authors have developed irregular fractions to provide resolution
V designs using fewer runs (Oehlert and Whitcomb, 2002). Among examples of these
irregular fractions are Jonh’s 3/4 fractions (Jonh, 1961), Addelman’ designs (Addelman,
1961) and minimum-run equireplicated
2
resolution V designs developed by Oehlert and
Whitcomb (2002). These designs allowed to signiﬁcantly reduce the number of experi-
mental runs while still enabling estimate of the second-order model. For further details
on the construction of these designs see (Oehlert and Whitcomb, 2002).
The selected statistical designs for the studies presented in Chapters 5 and 7 are in-
dicated in Table 4.5, along with the selected factors (mixture parameters), response
variables, α value, and total number of runs. Based on the Japanese SCC-designing
method six parameters are required to completely deﬁne the concrete mixtures (air con-
2
An equireplicated design is one in which each factor has an equal number of high and low levels.
98
4.7. Statistical design approach
Figure 4.14.: Number of runs (n
F
) as a function of number of factors (k) and de-
sign type (two-level). Color coding: grey=complete factorial design;
green=fractional design with resolution V or higher; yellow= fractional
design with resolution IV; red=fractional design with resolution III (State-
Ease Corporation, 2000)
tent was ﬁxed at 2%), while only four of these parameters are required to deﬁne mortar
mixtures (see Table 4.5). A complete 2
4
factorial design was selected for the studies
carried out at the mortar level (presented in Chapter 5). Since the studies at the con-
crete level involve a larger number of factors, only a fraction of the full factorial design
was used to form the central composite design. A regular 2
(5−1)
fractional factorial,
with resolution V, was selected for the ﬁrst study on concrete (presented in Chapter 7,
Section 7.6) and one of the factors was kept constant, due to limitations of materials.
A minimum-run equireplicated resolution V design (Oehlert and Whitcomb, 2002) was
selected for the second study on concrete (presented in Chapter 7, Section 7.7). This
allowed accommodation of all six mixture parameters as design factors. Further de-
tails on the ranges over which the factors were changed and the speciﬁc levels at which
experimental runs were carried out, are given in Chapters 5 and 7.
4.7.3. Performing the experiments
Statistical methods in experimental design require that the observations (or errors)
be independently distributed random variables (Montgomery, 2001). Thus, both the
allocation of experimental materials and the order in which the individual runs are to
be performed should be randomly determined. It is recommended to perform a few trial
runs before conducting the experiment. This can be useful to check the adequacy of
the selected range of mixture parameters, to check on the measurement system and to
practice the overall experimental technique. During the experiment, it is also useful to
99
4. SCC mix-design and properties
Table 4.5.: Summary of selected statistical designs
Reference Experimental Design Factors Other Parameters Responses
Chapter 5 CCD: V
w
/V
p
; α = 2, 0 Slump ﬂow test (Dﬂow);
(mortar 2
4
factorial design w/c; V-funnel test (Tfunnel);
mixtures) augmented with 8 axial Sp/p; 28 runs 28 days compressive
runs plus 4 central runs V
s
/V
m
strength test (fc,28)
Chapter 7 CCD: V
w
/V
p
; α = 2, 0 Slump ﬂow test
(concrete 2
(5−1)
fractional factorial w
f
/w
c
; (Dﬂow, T50);
mixtures) design (resolution V) Sp/p; Vs/Vm was V-funnel test (Tfunnel);
augmented with 10 axial s
1
/s; kept constant Box-test (H); 28
runs plus 4 central runs V
g
/V
g,lim
days compressive
30 runs strength test (fc,28)
Chapter 7 CCD: V
w
/V
p
; α = 1, 565 Slump ﬂow test
(concrete Minimum-run equireplicated w
f
/w
c
; (Dﬂow); V-funnel
mixtures) resolution V design Sp/p; 34 runs test (Tfunnel); L-box
augmented with 12 axial V
s
/V
m
; test (H2/H1);
runs plus 6 central runs s
1
/s; 28 days compressive
V
g
/V
g,lim
strength test (fc,28)
spread the replicates of the centre point out in time (an exception to the randomization
rule mentioned before); in this way the experimenter can get a rough check on the
stability of the process during the experiment.
4.7.4. Statistical analysis of data
In this work commercial software Design-Expert (State-Ease Corporation, 2000) was
used to analyse the results for each response variable, by examining summary plots of
the data, ﬁtting a model using regression analysis and analysis of variance (ANOVA),
validating the model by examining the residuals for trends, outliers and other undesired
features, and, ﬁnally, interpreting the model graphically.
Fitting a model
For each response variable, a quadratic model can be estimated from the central com-
posite design data. The generic form of a second order model is:
y = β
0
+
k
¸
i=1
β
i
x
i
+
k
¸
i=1
β
ii
x
2
i
+
¸
i<j
¸
β
ij
x
i
x
j
+ ε (4.21)
where y is the response; x
i
are the independent variables; β
0
is the independent term; β
i
,
β
ii
and β
ij
are the coeﬃcients of the independent variables and interactions, representing
100
4.7. Statistical design approach
their contribution to the response; ε is the random residual error term representing the
eﬀects of variables or higher order terms not considered in the model.
In general, any model that is linear in the parameters (the β values) can be regarded as
a multiple linear regression model of the type
y = β
0
+ β
1
x
1
+ β
2
x
2
+ . . . + β
k
x
k
+ ε (4.22)
where x
i
are called the predictor variables and the parameters β
j
are called the regres-
sion coeﬃcients. This applies to the second-order model in equation (4.21). The most
common method of estimating the regression coeﬃcients, in a multiple linear regression
model, is to use a Least Square Error approach (LSE). From the n > k observations
collected on the response variable y
1
, y
2
, . . . , y
n
, for each observation x
ij
on each of the
predictor variable deﬁned in the experimental design, the following n equations may be
established
y
i
= β
0
+
k
¸
i=1
β
j
x
ij
+ ε
i
i = 1, 2, . . . , n (4.23)
According to the LSE approach, the regression coeﬃcients are estimated by minimizing
the total sum of the squares of the errors (ε
i
in equation (4.23)). Thus, the LSE esti-
mators,
ˆ
β
0
,
ˆ
β
1
, . . . ,
ˆ
β
k
must satisfy
∂(
n
¸
i=1
ε
2
i
)
∂β
j

ˆ
β
0
,
ˆ
β
1
,...,
ˆ
β
k
= 0, j = 1, 2, . . . , k (4.24)
Finally, the ﬁtted model is given by
ˆ y =
ˆ
β
0
+
k
¸
j=1
ˆ
β
j
x
j
(4.25)
In multiple linear regression analysis, a series of hypothesis-testing procedures are per-
formed to evaluate the usefulness of the obtained model. Student’s t-test is performed to
identify non-signiﬁcant terms and analysis of variance (ANOVA) is used to evaluate the
regression model in several aspects (signiﬁcance of regression, lack of ﬁt and signiﬁcance
of each variable in the model).
101
4. SCC mix-design and properties
Student’s t-test
It may happen that, for some response variables, some of the terms in equation (4.25)
may not be signiﬁcant. The signiﬁcance of each predictor variable on a given response
can be evaluated using a Student’s t-test (Montgomery, 2001). The hypotheses for
testing the signiﬁcance of any individual regression coeﬃcient are
H
0
: β
j
= 0
H
1
: β
j
= 0
The test statistic for this test is
T
0
=
ˆ
β
j
se(
ˆ
β
j
)
(4.26)
where the denominator is the standard error of the coeﬃcient. H
0
is rejected (or the term
in the model is signiﬁcant) if when applying (4.26) to data one has |T
0
| > T
α/2, n−k−1
, where T
α/2, n−k−1
is a critical value calculated from a Student’s t-distribution with
parameter (n − k − 1). Alternatively, one can use the p-value approach to hypothesis
testing and thus reject H
0
if the p-value for the statistic T
0
is less than the chosen
signiﬁcance level (α). This test procedure is equivalent to the partial F test on a single
predictor variable, which will be exposed later.
When there are several candidate predictor variables in a regression model, one needs a
search procedure together with an appropriate criterion to select the predictor variables
that should be included in the model. The search proceeds in a stepwise manner,
by either adding consecutive variables to the model (the so-called Forward Elimination
method) or by removing variables from an initial set (the so-called Backward Elimination
method). Since any regression coeﬃcient depends on all the other predictor variables
that are in the model, one can easily conclude that the ﬁnal ﬁtted model may diﬀer
slightly depending on the selected search method. In the present work, a step-by-step
backward elimination was used to eliminate non-signiﬁcant terms in the regression model
(State-Ease Corporation, 2000), i.e. those terms associated with a p-value greater than
the chosen signiﬁcance level. In this study, the criteria used for the entry and removal
of a variable in the model was α < 0,05 and α >0,10, respectively.
ANOVA tests
The basic ANOVA test for signiﬁcance of regression is to determine if there is a linear
relationship between the response variable y and a subset of predictor variables. The
appropriate hypotheses are:
102
4.7. Statistical design approach
H
0
: β
1
= β
2
= . . . = β
k
= 0
H
1
: β
j
= 0 for at least one j
This test involves breaking the total deviation of the observations around the mean
(SS
T
) into two components: the deviations of the ﬁtted values around the mean, known
as regression sum of squares (SS
R
); and the error sum of squares (SS
E
), which represents
the deviations of the observations around the regression ﬁtted model. This test is usually
summarized in an ANOVA table such as Table 4.6. This test consists on computing the
value of the test statistic F
0
over the available data (see Table 4.6) and to reject H
0
if
the value of F
0
exceeds F
α, k, n−k−1
, the critical value computed from a Fisher-Snedecor
distribution with parameters (k) and (n −k −1) .
A measure of the amount of reduction in the variability of the response y obtained by
using the regressor variables x
1
, x
2
, . . . , x
k
in the model is given by the coeﬃcient of
multiple determination R
2
deﬁned as
R
2
=
SS
R
SS
T
= 1 −
SS
E
SS
T
(4.27)
Adding a variable to the model (whether it is statistically signiﬁcant or not) will always
increase R
2
. For this reason an R
2
adj
statistic is sometimes preferred, being deﬁned as
R
2
adj
= 1 −
SS
E
/(n −p)
SS
T
/(n −1)
(4.28)
where p = k + 1 is the number of regression coeﬃcients. Both measures are given for
the models presented in Chapters 5 and 7.
As it was mentioned before, adding replicates of centre point to a factorial design allows
obtaining an estimate of pure experimental error. This permits dividing the residual
sum of squares (SS
E
) into two components: the deviation due to pure error (SS
PE
) and
the deviation due to lack of ﬁt (SS
LOF
), computed as indicated in Table 4.6. In the
expressions of SS
PE
and SS
E
, y
ij
denotes the jth observation on the response at x
i
, i =
1, 2, . . . , m and j = 1, 2, . . . , n
i
. Thus, there are n =
¸
m
i=1
n
i
total observations. If there
is a lack of ﬁt, SS
LOF
will dominate SS
E
, compared with SS
PE
. Therefore, the Lack of
ﬁt test procedure consists on computing the value of the test statistic F
0
on the available
data (see Table 4.6) and deciding whether the hypothesis of the regression function to
be linear should be rejected or not. If the value of F
0
exceeds F
α, m−p, n−m
, the critical
value computed from a Fisher-Snedecor distribution with parameters (m−p, n −m).
The partial F test can be used to measure the contribution of the predictor variable
x
j
as if it was the last variable added to the model. It involves computing the “extra
sum of squares” due to β
j
, which is the increase in the regression sum of squares due to
103
4. SCC mix-design and properties
T
a
b
l
e
4
.
6
.
:
A
n
a
l
y
s
i
s
o
f
v
a
r
i
a
n
c
e
t
e
s
t
i
n
m
u
l
t
i
p
l
e
r
e
g
r
e
s
s
i
o
n
(
M
o
n
t
g
o
m
e
r
y
,
2
0
0
1
)
S
o
u
r
c
e
o
f
S
u
m
o
f
D
e
g
r
e
e
s
o
f
M
e
a
n
T
e
s
t
f
o
r
:
v
a
r
i
a
t
i
o
n
s
q
u
a
r
e
s
f
r
e
e
d
o
m
s
q
u
a
r
e
F
v
a
l
u
e
-
s
i
g
n
i
ﬁ
c
a
n
c
e
o
f
r
e
g
r
e
s
s
i
o
n
S
S
R
=
¸
n i
=
1
(
ˆy
i
−
¯y
i
)
2
k
M
S
R
=
S
S
R
/
k
F
0
=
M
S
R
M
S
E
r
e
g
r
e
s
s
i
o
n
e
r
r
o
r
o
r
r
e
s
i
d
u
a
l
S
S
E
=
¸
n i
=
1
(
y
i
−
ˆy
i
)
2
n
−
k
−
1
M
S
E
=
S
S
E
/
(
n
−
k
−
1
)
t
o
t
a
l
S
S
T
=
¸
n i
=
1
(
y
i
−
¯y
i
)
2
=
S
S
R
+
S
S
E
n
−
1
-
l
a
c
k
o
f
ﬁ
t
l
a
c
k
o
f
ﬁ
t
S
S
L
O
F
=
¸
m i
=
1
n
i
(
¯y
i
−
ˆy
i
)
2
m
−
p
M
S
L
O
F
=
S
S
L
O
F
/
(
m
−
p
)
F
0
=
M
S
L
O
F
M
S
P
E
p
u
r
e
e
r
r
o
r
S
S
P
E
=
¸
m i
=
1
¸
n
i
j
=
1
(
y
i
j
−
¯y
i
)
2
n
−
m
M
S
P
E
=
S
S
P
E
/
(
n
−
m
)
e
r
r
o
r
o
r
r
e
s
i
d
u
a
l
S
S
E
=
¸
m i
=
1
¸
n
i
j
=
1
(
y
i
j
−
ˆy
i
)
2
n
−
p
-
p
a
r
t
i
a
l
s
i
g
n
i
ﬁ
c
a
n
c
e
o
f
r
e
g
r
e
s
s
i
o
n
(
r
e
d
u
c
e
d
m
o
d
e
l
)
S
S
R
(
β
j
|
β
0
,
β
1
,
.
.
.
,
β
j
−
1
,
β
j
+
1
,
.
.
.
,
β
k
)
1
M
S
R
=
S
S
R
/
1
F
0
=
M
S
R
M
S
E
e
a
c
h
p
r
e
d
i
c
t
o
r
v
a
r
i
a
b
l
e
e
r
r
o
r
(
f
u
l
l
m
o
d
e
l
)
S
S
E
=
¸
n i
=
1
(
y
i
−
ˆy
i
)
2
n
−
k
−
1
M
S
E
=
S
S
E
/
(
n
−
k
−
1
)
104
4.7. Statistical design approach
adding x
j
to a model that already includes x
1
, . . . , x
j−1
, x
j+1
, . . . , x
k
. This test consists
on computing the value of F
0
over the available data (see Table 4.6) and to reject H
0
if the value of F
0
exceeds F
α,1,n−p
, the critical value computed from a Fisher-Snedecor
distribution with parameters (1, n −p).
Alternatively, in all of these Fisher-tests one can use the p-value approach to hypothesis
testing and thus reject H
0
if the p-value for the statistic F
0
regarding the available data
is less than α.
Model adequacy checking
The hypothesis testing involved in regression analysis is based on the assumptions that
the errors (ε
i
) are independent and normally distributed with zero mean and constant
variance. In order to assess these assumptions one can use graphical inspection and per-
form the Normal distribution ﬁtting tests. In the present work the Normal probability
plot of residuals were examined to check normality, the plots of residuals against pre-
dicted values and the plot of residual against values of the individual predictor variables
were analysed to check the constancy of residual variance. In addition, the Lilliefors
test for normality (a modiﬁcation of Kolmogorov-Smirnov test of goodness of ﬁt) and
Shapiro-Wilk test for normality were performed. The Durbin-Watson test statistic was
used to detect the presence of autocorrelation in the residuals from the regression anal-
ysis (Gunst and Mason, 1980). In addition, the randomness of residuals was evaluated
by computing autocorrelations for data at varying time lags. Autocorrelations should
be near zero for all time-lag separations, if not the randomness assumption fails. In
case one or more of the autocorrelations will be signiﬁcantly non-zero, an autoregressive
model might be appropriate and a partial autocorrelation plot can be examined to help
identify the order of the autoregressive model (Box et al., 1978).
If needed, a transformation of the response variable is often an eﬀective method for
stabilizing the response variance, making the distribution of the response variable closer
to the normal distribution, and improving the ﬁt of the model to the data. The selection
of the form of the transformation can be made with the help of Box-Cox criteria which
uses an estimate of λ for power transformations of the type y∗ = y
λ
or by trial–and–error
(Montgomery, 2001). Besides the large family of power transformations, the lny and
log y can also be considered.
Another important issue is the detection of regression outliers. Regression outliers cor-
respond to cases exhibiting a strong deviation from the ﬁtted regression curve, which
can have a harmful inﬂuence in the process of ﬁtting the model to the data. Points with
this characteristic are named leverage points. Identiﬁcation of leverage points, for their
eventual removal from the dataset, was carried out by analysing the Cook’s distance.
105
4. SCC mix-design and properties
If there are no outliers, these distances are of approximately equal amplitude and less
than 1,0 (Montgomery, 2001).
The “best” regression models are those in which the predictor variables each correlate
highly with the dependent variable but correlate at most only minimally with each
other. The existence of high correlation between the predictor variables (multicollinear-
ity) leads to imprecise determination coeﬃcients, imprecise estimates and imprecise tests
on the regression coeﬃcients. Multicollinearity was assessed by using the so-called vari-
ance inﬂation factors (V IF), which are deﬁned for each predictor variable as
V IF
k
= (1 −r
2
k
)
−1
(4.29)
where r
2
k
is the coeﬃcient of multiple determination when x
k
is regressed on the re-
maining variables in the model. An r
2
k
near 1, indicating higher correlation with the
remaining variables will result in a large value of V IF. A V IF larger than 10 is usually
taken as an indicator of multicollinearity.
An exempliﬁcation of test procedures for examining hypotheses about multiple linear
regression models and techniques for checking model assumptions, described here, is
given in Appendix D for the ﬁrst model appearing in Chapter 5, paragraph 5.3.1.
4.7.5. Mixtures optimisation
Commercial software Design-Expert (State-Ease Corporation, 2000) also allows the si-
multaneous optimization of multiple responses obtained from the factorial experimental
plan. Numerical optimization can optimize any combination of one or more goals. The
allowable goals are to minimize or maximize a parameter, to target a speciﬁc level of
a parameter, to keep a parameter within a speciﬁed range or none (the default goal is
to keep the parameter within the low and high limits) (State-Ease Corporation, 2000).
Each goal is assigned a weight (number between 1 and 5 with 5 being the most im-
portant and 1 the least important) and goals are combined in an overall desirability
function. The optimization software searches for the greatest overall desirability. A
value of one of the desirability function represents the ideal case. A zero indicates that
one or more responses fall outside desirable limits. The goal seeking begins at a random
starting point and proceeds up the steepest slope to a maximum. There may be two
or more maxima because of curvature in the response surfaces and their combination
into the desirability function. Starting from several points in the desiring space may be
necessary to ﬁnd the best local maximum.
Speciﬁc details on optimization criteria used in diﬀerent studies are given in Chapters
5 and 7.
106
4.7. Statistical design approach
4.7.6. Comments on this approach
The experimental design approach provides a way to evaluate the eﬀects of mixture pro-
portions in a statistically sound manner with a minimum number of mixtures. The sta-
tistical concepts involved are well known and widely used in many industries. However,
some prior knowledge is required to select the values of factors used in the experimental
design such that all or most mixtures exhibit SCC or near-SCC ﬂow characteristics.
In this approach deliberate laboratory testing is conducted with actual job materials to
measure the response variables. Regression models can be ﬁtted to the results of each
measured response, which allow establishing ﬁnal mixture proportions eﬃciently. As a
rule, the resulting models are speciﬁc to only the materials and range of proportions
considered. Nevertheless, the general relative trends found for a certain set of materials
and proportions may remain consistent when a diﬀerent set of materials is used.
107
4. SCC mix-design and properties
108
5. Optimization of SCC mortar
mixtures
5.1. Introduction
Since the mortar properties adequate for SCC were suﬃciently well deﬁned during the
early development of SCC at the University of Tokyo (Okamura et al., 2000), initial
tests are often carried out at mortar level. Not only mortar tests are easier to carry
out, and less time consuming when compared to tests with concrete, but if target values
are achieved at mortar level, the tests on concrete, although essential, may be reduced
to a minimum (Okamura et al., 2000). This chapter presents the methodology used
for the design of mortar mixtures which are adequate for SCC. This methodology was
developed in three phases: ﬁrst, the experimental phase conducted according to a cen-
tral composite design; second, the statistical analysis and model ﬁtting of data collected
during the experimental phase and, third, the numerical optimization of mixture param-
eters using the models derived in the previous phase. This methodology was applied to
six diﬀerent types of cement in combination with limestone ﬁller and a polycarboxylate
type superplasticizer. Contour plots and interaction diagrams representing the range of
mixture parameters where SCC can be found are presented for each cement type. These
plots and diagrams can be useful to simplify the test protocol required to optimize a
given SCC mixture.
This chapter provides reference mortar mixtures for the study on cement variations
which is reported in Chapter 6.
5.2. Experimental programme
5.2.1. Materials characterization
Mortar mixes investigated in this study were prepared with cement (ﬁrst delivery), a
mineral additive, limestone ﬁller (ﬁrst delivery), reference sand conforming to CEN EN
109
5. Optimization of SCC mortar mixtures
196-1 and tap water. Six of the most currently used cement types in Portuguese con-
struction industry were selected. The chemical and physical properties of the diﬀerent
cement types and limestone ﬁller are presented in Tables C.1 to C.7 of Appendix C.
The mean particle size of limestone ﬁller was 4,5 µm. A polycarboxylate type super-
plasticizer (V3000) was used having a speciﬁc gravity of 1,05 and 18,5% solid content.
Reference sand is a siliceous round natural sand (0,08-2 mm) with a speciﬁc gravity of
2,57 and an absorption value of 0,68%.
5.2.2. Experimental plan
Since curvature on the response surface of mortar properties was found by other authors
(Nehdi and Summer, 2002), in the present study experiments were designed according to
a central composite design adequate to ﬁt a second order model (see paragraph 4.7.4).
This design consisted of a 2
4
factorial statistical design (four factors at two levels)
augmented with 8 axial runs plus 4 central runs to evaluate the experimental error. SCC
mortar mix proportions were established based on the following variables x
i
: water to
powder volume ratio (V
w
/V
p
); water to cement weight ratio (w/c); superplasticizer to
powder weight ratio (Sp/p); sand to mortar volume (V
s
/V
m
), as suggested by Okamura
et al. (2000). The eﬀect of each variable was evaluated at ﬁve diﬀerent levels −α, –1, 0,
+1, +α as presented in Table 5.1 and the design was made rotatable by taking α equal
to 2,0.
The absolute value of each variable corresponding to a given level in Table 5.1 depends
on the variable itself and on the speciﬁc experimental plan. For a given variable and
a given experimental plan the transformation of coded into absolute values is of the form:
a = a
0
+ x · ∆a (5.1)
with x being the coded variable measured with the step like units, a the absolute value
in normal units, a
0
the absolute value of the variable at the centre of the design and ∆a
the variable variation (in absolute values) corresponding to a unit change in the coded
variable.
In this work, experimental mixtures F8 and F9 (see Table 5.1) were ﬁrst assessed and
their results were analyzed before proceeding with the rest of the experimental plan, in an
attempt to check the adequacy of the selected range of mixture parameters. Considering
the combination of mixture levels in the F8 and F9 mixes, these should respectively lead
to one of the most ﬂuid mixtures and to one of the least ﬂuid mixtures, of all mixtures
in the experimental plan. Thus, it is desirable that the interval formed by the results
obtained from F8 and F9 mixtures includes the target value for each mortar test which
are a Dﬂow of 260 mm and a Tfunnel of 10 s, in the present work. When this is
110
5.2. Experimental programme
Table 5.1.: Coded values for the variables used in the experimental design
Ref. point type V
w
/V
p
w/c Sp/p V
s
/V
m
Ci
a
central 0 0 0 0
F1 factorial -1 -1 -1 -1
F2 1 -1 -1 -1
F3 -1 1 -1 -1
F4 1 1 -1 -1
F5 -1 -1 1 -1
F6 1 -1 1 -1
F7 -1 1 1 -1
F8 1 1 1 -1
F9 -1 -1 -1 1
F10 1 -1 -1 1
F11 -1 1 -1 1
F12 1 1 -1 1
F13 -1 -1 1 1
F14 1 -1 1 1
F15 -1 1 1 1
F16 1 1 1 1
CC1 axial 2 0 0 0
CC2 -2 0 0 0
CC3 0 2 0 0
CC4 0 -2 0 0
CC5 0 0 2 0
CC6 0 0 -2 0
CC7 0 0 0 2
CC8 0 0 0 -2
a
the central point was replicated four times (i=1 to 4)
111
5. Optimization of SCC mortar mixtures
not the case, the range of one or more variables should be changed, i.e. the values of
a
0
, ∆a or both should be altered. In the present work, the plan was modiﬁed when
changing from cement type CEM I 42,5 R to CEM II/B-L 32,5 N, CEM IV/B(V) 32,5
N and CEM II/B-L 32,5 R (BR). The values of a
0
and ∆a adopted in each experimental
plan, corresponding to the diﬀerent cement types, are given in Table 5.2. Hence, the
modelled experimental region in each experimental plan consisted of mixtures made
with the variable ranges presented in Table 5.3.
5.2.3. Mixing sequence and testing methods
With the exception of F8 and F9, mixtures from the experimental plan were tested in a
random order. The mixes were prepared in the laboratory in 1,4 l batches and mixed in a
two-speed mixer complying to NP EN 196-1(Portugal. IPQ, 2006). The mixing sequence
consisted of mixing sand and powder materials with 0,81 of the mixing water during 60
s, stopping the mixer to scrape material adhering to the bowl of the mixing bowl, mixing
for another 60 s, adding the rest of the water with the superplasticizer, mixing for 60 s,
stopping the mixer again to scrape material adhering to the bowl, mixing for another 30
s, stopping the mixer for 5 min (adequate for V3000 superplasticizer type) and ﬁnally
mixing mortar during a further 30 s. The mixer was always set at low speed except
in the last 30 s of the mixing sequence, when it was set as high speed. Mortar tests
using the ﬂow cone and the V-funnel, with the same internal dimensions as the Japanese
equipment (see paragraph 4.6.2), were then carried out to characterize fresh state. After
fresh mortar tests, three 70 mm cubes were moulded to evaluate 28 days compressive
strength (fc,28). Mortar cubes were demoulded one day after casting and kept inside a
chamber under controlled environmental conditions (Temperature=20
◦
C and HR=95-
98%) until testing age. The mortar ﬂow test was used to assess deformability through
the calculation of the ﬂow diameter (Dﬂow) as the mean of two opposite diameters in
the spread area. The V-funnel test was used to assess the viscosity and passing ability
of the mortar. Test ﬂow time was recorded (Tfunnel). Dﬂow, Tfunnel and fc,28 were
the selected mortar properties to be analysed and modelled.
5.3. Collected data, ﬁtted models and mixtures
optimization
The mix proportions and test results of the 28 mixes prepared as described in para-
graph 5.2.3 are summarized in Tables E.1, E.2, E.3, E.4, E.5 and E.6, of Appendix E,
for the experimental plans incorporating CEM I 42,5 R, CEM I 52,5 R, CEM II/A-L
42,5 R, CEM II/A-L 32,5 N, CEM IV/B (V) 32,5 N and CEM II/B-L 32,5 R (BR),
112
5.3. Collected data, ﬁtted models and mixtures optimization
Table 5.2.: Values a
0
and ∆a used in the transformation of coded values into absolute
values of the variables in each experimental plan
V
w
/V
p
w/c Sp/p V
s
/V
m
a
0
∆a a
0
∆a a
0
∆a a
0
∆a
CEM I 52,5 R 0,900 0,40 1,75%
CEM I 42,5 R 0,900 0,40 1,75%
CEM II/A-L 42,5 R 0,900 0,100 0,40 0,05 1,75% 0,25% 0,45 0,025
CEM II/B-L 32,5 N 0,850 0,40 1,75%
CEM IV/B(V) 32,5N 0,800 0,42 1,75%
CEM II/B-L 32,5R (BR) 0,700 0,38 1,25%
Table 5.3.: Correspondence between coded values and absolute variable values in each
experimental plan
variable -2 -1 0 +1 +2
CEM I 42,5 R; CEM I 52,5 R; CEM II/A-L 42,5 R
V
w
/V
p
0,700 0,800 0,900 1,000 1,100
w/c 0,30 0,35 0,40 0,45 0,50
Sp/p 1,25% 1,50% 1,75% 2,00% 2,25%
V
s
/V
m
0,400 0,425 0,450 0,475 0,500
CEM II/B-L 32,5 N
V
w
/V
p
0,650 0,750 0,850 0,950 1,050
w/c 0,30 0,35 0,40 0,45 0,50
Sp/p 1,25% 1,50% 1,75% 2,00% 2,25%
V
s
/V
m
0,400 0,425 0,450 0,475 0,500
CEM IV/B(V) 32,5 N
V
w
/V
p
0,600 0,700 0,800 0,900 1,000
w/c 0,32 0,37 0,42 0,47 0,52
Sp/p 1,25% 1,50% 1,75% 2,00% 2,25%
V
s
/V
m
0,4 0,425 0,45 0,475 0,5
CEM II/B-L 32,5 R (BR)
V
w
/V
p
0,500 0,600 0,700 0,800 0,900
w/c 0,28 0,33 0,38 0,43 0,48
Sp/p 0,75% 1,00% 1,25% 1,50% 1,75%
V
s
/V
m
0,400 0,425 0,450 0,475 0,500
113
5. Optimization of SCC mortar mixtures
respectively. Statistics of the obtained results were computed for all data points and
also for the central points only (see paragraphs 5.3.1 to 5.3.6). From these results it may
be observed that with these experimental plans a wide range of mortars was covered.
Moreover, the range of Dﬂow and Tfunnel results obtained is adequate since it includes
both targets Dﬂow and Tfunnel. None of the mixes exhibited severe segregation. From
the central points data it can be concluded that the highest experimental error is associ-
ated with the Tfunnel response variable, with exception of experimental plans including
CEM IV/B (V) 32,5 N and CEM II/B-L 32,5 R (BR).
As explained in paragraph 4.7.4, for each response variable a quadratic model can be
estimated from the central composite design data (see equation 4.21). The model pa-
rameters (β
0
, β
j
, β
ij
) are estimated by means of a multilinear regression analysis. The
estimates of the ﬁtted models, including the residual error term, along with the re-
gression coeﬃcients, are given in the following paragraphs for each cement type. An
analysis of variance showed that these models are signiﬁcant when describing the eﬀect
of V
w
/V
p
, w/c, Sp/p and V
s
/V
m
on the modelled responses. The estimates of the model
coeﬃcients, obtained for the coded variables, give an indication of the relative signif-
icance of the various mixture variables on each response. A negative estimate means
that the response variable will decrease if the given mixture variable increases. The
most signiﬁcant individual and interaction eﬀects found on analysed responses are listed
in paragraphs 5.3.1 to 5.3.6, for each cement type.
Residual analysis did not reveal obvious model inadequacies or indicate serious violations
of the normality assumptions, except in the case of Tfunnel response. This problem
was overcome after a variable transformation of the form y
−0,5
. No evidence of auto-
correlation or unstationarity was found in the residues.
In general, the estimated residual standard deviation does not exceeded the experimental
error by far, so a good ﬁtting can be expected. Three mixtures covering a wide range
of proportioning and not used to derive the model (mixes 29 to 31 in Tables E.1 to E.6,
in Appendix E) were selected to verify the ability of the proposed models to predict
the responses. The four central point mixtures (mixes 1 to 4 in Tables E.1 to E.6, in
Appendix E) were used along with these three mixtures to compare the measured-to-
predicted values of each response variable. A plot of measured-to-predicted values of
Dﬂow, Tfunnel and fc,28 are shown in paragraphs 5.3.1 to 5.3.6, for each experimental
plan, with the prediction intervals corresponding to a 95% conﬁdence level. One can
observe that all points (including those not used to derive the models) fall within the
limits of the prediction intervals. In addition, the ratio between predicted-to-measured
values for Dﬂow, Tfunnel and fc,28 (central points and additional data) was not far
from 1,0. Thus, one can expect the established models to be suﬃciently accurate to
predict the analysed fresh and hardened properties, with the possible exception of fc,28
114
5.3. Collected data, ﬁtted models and mixtures optimization
Table 5.4.: Optimization constraints
Lower Higher Lower Higher
Variable Goal limit limit weight weight Importance
V
w
/V
p
is in range -2,5 2,5 1 1 3
w/c is equal to c
1
a
-2,5 2,5 1 1 3
Sp/p is in range -2,5 2,5 1 1 3
V
s
/V
m
is equal to c
2
b
-2,5 2,5 1 1 3
Dﬂow (mm) is target = 260 240 280 1 1 3
Tfunnel (s) is target = 10 8 12 1 1 3
a
c
1
varying from 0,3 to 0,5 by steps of 0,01;
b
c
2
varying from 0,4 to 0,5 by steps of 0,01
of mortars including CEM I 52,5 R (see paragraph 5.3.2). Nevertheless, this model will
still be useful, explaining more than 50 % of the variations in the response.
After building the regression models that establish relationships between mix design
variables and the responses, the numerical optimization technique was used to determine
the range of mortar mixture parameters where deformability and viscosity coexist in a
balanced manner, i.e. to determine the best mixtures which exhibit a spread ﬂow of 260
mm and a ﬂow time of 10 s. In the present work, a slightly higher target spread ﬂow value
was adopted when compared to the value recommended by the Japanese SCC-designing
method, based on previous experience of the author and EFNARC (2002). The values
of V
w
/V
p
and Sp/p that can lead to the best mixtures were searched for a given pair
of (V
s
/V
m
; w/c ) by using the constraints presented in Table 5.4. No restriction was
established for the fc,28 response variable. Note that since the response models were
expressed as a function of four independent variables, a multiple optimum will hardly
occur. Because the error in predicting the responses increases with the distance from
the centre of the modelled region, the use of the models was limited to an area bound
by coded values + 2,5 to -2,5. Finally, the adjusted values of V
w
/V
p
and Sp/p and the
estimated values of fc,28, for each pair of (V
s
/V
m
; w/c ), were used to obtain the contour
plots presented in paragraphs 5.3.1 to 5.3.6.
5.3.1. CEM I 42,5 R
The experimental plan corresponding to CEM I 42,5 R was carried out between the 18
th
and 20
th
of January 2006. A cement sample was taken from the ﬁrst delivery of CEM I
42,5 R supplied by Cimpor, from the Alhandra production centre.
Statistics of the obtained results are presented in Table 5.5 for all data points and also for
the central points only. The range of mortar properties covered with this experimental
plan was: Dﬂow ranging from 197 to 336 mm, Tfunnel ranging from 2 to 12 s and
115
5. Optimization of SCC mortar mixtures
Table 5.5.: Statistics of the results for the total points and for central points (CEM I
42,5 R)
N=28 total points n
c
=4 central points
Dﬂow Tfunnel fc,28 Dﬂow Tfunnel fc,28
(mm) (s) (MPa) (mm) (s) (MPa)
Minimum 197 1,8 61,3 278 3,72 68,1
Maximum 336 11,7 79,2 283 4,1 72,7
Mean 275 4,9 72,5 280 4,0 70,9
Standard deviation 36 2,7 4,3 3 0,2 2,0
Coeﬃcient of variation 13,1% 55,5% 6,0% 0,9% 4,9% 2,8%
fc,28 ranging from 61 to 79 MPa. The estimates of the ﬁtted models, including the
residual error term, along with the correlation coeﬃcients, are given in Table 5.6. It can
be observed that the estimated residual standard deviation (see Table 5.6) does not
exceed the experimental error by far (see Table 5.5). The measured-to-predicted values
of Dﬂow, Tfunnel and fc,28 are shown in Figures 5.1, 5.2 and 5.3, respectively, with
the prediction intervals corresponding to a 95% conﬁdence level. The ratio between
predicted-to-measured values (central points and additional data) for Dﬂow, Tfunnel
and fc,28 ranged between 1,00 and 1,05, 0,95 and 1,06, 0,97 and 1,06, respectively.
The results in Table 5.6 clearly show that w/c exhibit a great eﬀect on all three mea-
sured responses, being only exceeded by the eﬀect of V
w
/V
p
on (Tfunnel
−0,5
) response.
Besides w/c, the variable that most inﬂuenced Dﬂow was Sp/p. Signiﬁcant interaction
eﬀects were found between V
w
/V
p
and w/c on all the analysed responses, and in the case
of fc,28 response this eﬀect was larger than the individual eﬀects of V
w
/V
p
, Sp/p and
V
s
/V
m
. This interaction eﬀect is related to the ﬁller content. Interaction eﬀects between
V
w
/V
p
and Sp/p and between w/c and V
s
/V
m
were also found to be signiﬁcant on Dﬂow
response. The quadratic term in w/c was signiﬁcant for both Dﬂow and Tfunnel re-
sponses. The quadratic term in Sp/p was signiﬁcant for both Dﬂow and fc,28 responses.
The quadratic term in V
s
/V
m
was signiﬁcant on fc,28 response.
The adjusted values of V
w
/V
p
and Sp/p for each pair of (V
s
/V
m
, w/c) in optimized
mortars containing CEM I 42,5 R were used to obtain the contour plots presented
in Figure 5.4 (a). The corresponding contour plot for estimated mortar compressive
strength is presented in Figure 5.4 (b).
5.3.2. CEM I 52,5 R
The experimental plan corresponding to CEM I 52,5 R was carried out between the 16
th
and 18
th
of January 2006. A cement sample was taken from the ﬁrst delivery of CEM I
52,5 R supplied by Cimpor, from the Alhandra production centre.
116
5.3. Collected data, ﬁtted models and mixtures optimization
Table 5.6.: Fitted numerical models for coded variables (CEM I 42,5 R)
Response Dﬂow fc,28
variable (mm) [Tfunnel (s)]
−0,5
(MPa)
model terms estimate
independent 282,67 0,504 70,862
V
w
/V
p
11,16 0,093 -0,243
w/c 28,03 0,078 -3,543
Sp/p 18,51 0,022 1,014
V
s
/V
m
-11,82 -0,045 0,054
(V
w
/V
p
)×(w/c) 1,92 0,015 1,796
(V
w
/V
p
)×(Sp/p) -1,92 NS NS
(V
w
/V
p
)×(V
s
/V
m
) NS -0,006 NS
(w/c)×(V
s
/V
m
) 2,11 NS NS
(w/c)
2
-6,16 -0,009 NS
(Sp/p)
2
-2,41 NS 1,187
(V
s
/V
m
)
2
NS NS 0,689
residual error, ε
a
mean 0 0 0
standard deviation 3,072 0,011
b
1,734
R
2
0,993 0,992 0,839
R
2
adj
0,989 0,989 0,783
(NS) non-signiﬁcant terms;
a
error term is a random and normally distributed variable;
b
corresponding value for Tfunnel is 0,27
Figure 5.1.: Comparison of measured versus predicted values of Dﬂow (CEM I 42,5 R)
117
5. Optimization of SCC mortar mixtures
Figure 5.2.: Comparison of measured versus predicted values of Tfunnel (CEM I 42,5
R)
Figure 5.3.: Comparison of measured versus predicted values of fc,28 (CEM I 42,5 R)
118
5.3. Collected data, ﬁtted models and mixtures optimization
(a) (b)
Figure 5.4.: Optimized mortars incorporating CEM I 42,5 R: (a) range of mixture vari-
ables and (b) estimated values of fc,28 (MPa)
Statistics of the obtained results are presented in Table 5.7 for all data points and for
the central points only. The range of mortar properties covered with this experimental
plan was: Dﬂow ranging from 168 to 311 mm, Tfunnel ranging from 2 to 16 s and fc,28
ranging from 67 to 87 MPa. The estimates of the ﬁtted models, including the residual
error term, along with the correlation coeﬃcients, are given in Table 5.8. The estimated
residual standard deviation (see Table 5.8) was always lower than the experimental error
(see Table 5.7), except in the case of fc,28. Furthermore, the low regression coeﬃcient
of the fc,28 response indicates that the obtained model for fc,28 does not fully explains
the variation in the response as a result of V
w
/V
p
, w/c, Sp/p and V
s
/V
m
variations.
The measured-to-predicted values of Dﬂow, Tfunnel and fc,28 are shown in Figures 5.5,
5.6 and 5.7, respectively, with the prediction intervals corresponding to a 95% conﬁdence
level. The ratio between predicted-to-measured values (central points and additional
data) for Dﬂow, Tfunnel and fc,28 ranged between 0,99 and 1,04, 0,96 and 1,08, 0,98
and 1,03, respectively. Due to the low regression coeﬃcient of fc,28 response model the
prediction intervals are expected to be large in the case of fc,28 as it is observed in
Figure 5.7.
The results in Table 5.8 clearly show that w/c exhibit a great eﬀect on all three measured
responses, being only exceeded by the eﬀect of V
w
/V
p
on (Tfunnel
−0,5
) response. Besides
w/c the variable that most inﬂuenced Dﬂow was Sp/p. Signiﬁcant interaction eﬀects
were found between V
w
/V
p
and w/c on (Tfunnel
−0,5
) and fc,28 responses. Interaction
eﬀects between w/c and Sp/p and between Sp/p and V
s
/V
m
were found to be signiﬁcant
119
5. Optimization of SCC mortar mixtures
Table 5.7.: Statistics of the results for the total points and for central points (CEM I
52,5 R)
N=28 total points n
c
=4 central points
Dﬂow Tfunnel fc,28 Dﬂow Tfunnel fc,28
(mm) (s) (MPa) (mm) (s) (MPa)
Minimum 168 2,0 66,5 250 4,1 73,1
Maximum 311 15,9 86,6 256 4,6 76,8
Mean 251 5,8 75,5 253 4,4 75,5
Standard deviation 37 3,6 4,9 3 0,2 1,6
Coeﬃcient of variation 14,8% 62,8% 6,5% 1,2% 5,4% 2,2%
Table 5.8.: Fitted numerical models for coded variables (CEM I 52,5 R)
Response Dﬂow fc,28
variable (mm) [Tfunnel (s)]
−0,5
(MPa)
model terms estimate
independent 253,75 0,474 75,459
V
w
/V
p
8,52 0,093 -0,283
w/c 32,54 0,083 -3,418
Sp/p 16,02 0,018 NS
V
s
/V
m
-10,85 -0,044 -1,488
(V
w
/V
p
)×(w/c) NS 0,011 1,533
(V
w
/V
p
)×(V
s
/V
m
) NS -0,008 NS
(w/c)×(Sp/p) -1,69 NS NS
(w/c)×(V
s
/V
m
) NS -0,006 NS
(Sp/p)×(V
s
/V
m
) 2,13 NS NS
(V
w
/V
p
)
2
1,22 0,004 NS
(w/c)
2
-4,59 -0,011 NS
residual error, ε
a
mean 0 0 0
standard deviation 2,965 0,009
b
3,149
R
2
0,994 0,995 0,582
R
2
adj
0,991 0,993 0,509
(NS) non-signiﬁcant terms;
a
error term is a random and normally distributed variable;
b
corresponding value for Tfunnel is 0,19
120
5.3. Collected data, ﬁtted models and mixtures optimization
Figure 5.5.: Comparison of measured versus predicted values of Dﬂow (CEM I 52,5 R)
on Dﬂow response. Interaction eﬀects between V
w
/V
p
and V
s
/V
m
and between w/c
and V
s
/V
m
were also found to be signiﬁcant on (Tfunnel
−0,5
) response. The quadratic
terms in w/c and V
w
/V
p
were found to be signiﬁcant for both Dﬂow and (Tfunnel
−0,5
)
responses.
Contour plots representing the adjusted values of V
w
/V
p
and Sp/p for each pair of
(V
s
/V
m
; w/c) in optimized mortars containing CEM I 52,5 R are presented in Fig-
ure 5.8 (a). The corresponding contour plot for estimated mortar compressive strength
is presented in Figure 5.8 (b). A much lower number of optimized solutions was found
with the current experimental plan compared to the previous one. This is shown by the
reduced scale in both the x- and y-axis of Figure 5.8 (a).
5.3.3. CEM II/A-L 42,5 R
The experimental plan corresponding to CEM II/A-L 42,5 R was carried out between
the 11
th
and 13
th
of January 2006. A cement sample was taken from the ﬁrst delivery
of CEM II/A-L 42,5 R supplied by Cimpor, from the Alhandra production centre.
Statistics of the obtained results are presented in Table 5.9 for all data points as well as
for the central points only. The range of mortar properties covered with this experimen-
tal plan was: Dﬂow ranging from 215 to 349 mm, Tfunnel ranging from 2 to 10 s and
fc,28 ranging from 62 to 86 MPa. The range of Dﬂow results obtained is adequate but
the Tfunnel results were always below the target. The estimates of the ﬁtted models,
including the residual error term, along with the correlation coeﬃcients, are given in
Table 5.10. The estimated residual standard deviation (see Table 5.10) does not ex-
ceed the experimental error by far (see Table 5.9). The measured-to-predicted values
121
5. Optimization of SCC mortar mixtures
Figure 5.6.: Comparison of measured versus predicted values of Tfunnel (CEM I 52,5
R)
Figure 5.7.: Comparison of measured versus predicted values of fc,28 (CEM I 52,5 R)
122
5.3. Collected data, ﬁtted models and mixtures optimization
(a) (b)
Figure 5.8.: Optimized mortars incorporating CEM I 52,5 R (a) range of mixture vari-
ables and (b) estimated values of fc,28 (MPa)
of Dﬂow, Tfunnel and fc,28 are shown in Figures 5.9, 5.10 and 5.11, respectively, with
the prediction intervals corresponding to a 95% conﬁdence level. The ratio between
predicted-to-measured values (central points and additional data) for Dﬂow, Tfunnel
and fc,28 ranged between 1,00 and 1,02, 0,94 and 1,17, 0,98 and 1,07, respectively.
The results in Table 5.10 clearly show that w/c exhibit a great eﬀect on all three mea-
sured responses, being only exceeded by the eﬀect of V
w
/V
p
on (Tfunnel
−0,5
) response.
Besides w/c, the variable that most inﬂuenced Dﬂow was Sp/p. A signiﬁcant interaction
eﬀect was found between V
w
/V
p
and w/c on (Tfunnel
−0,5
) and fc,28 responses. An inter-
action eﬀect between V
w
/V
p
and V
s
/V
m
was also found to be signiﬁcant on (Tfunnel
−0,5
)
response. A signiﬁcant interaction eﬀect between w/c and Sp/p was found on Dﬂow
Table 5.9.: Statistics of the results for the total points and for central points (CEM
II/A-L 42,5 R)
N=28 total points n
c
=4 central points
Dﬂow Tfunnel fc,28 Dﬂow Tfunnel fc,28
(mm) (s) (MPa) (mm) (s) (MPa)
Minimum 215 1,7 62,5 286 3,3 72,8
Maximum 349 9,6 85,5 291 3,5 75,5
Mean 286 4,1 71,7 288 3,4 74,2
Standard deviation 32 2,2 5,4 2 0,1 1,1
Coeﬃcient of variation 11,2% 52,7% 7,6% 0,8% 2,0% 1,5%
123
5. Optimization of SCC mortar mixtures
Table 5.10.: Fitted numerical models for coded variables (CEM II/A-L 42,5 R)
Response Dﬂow fc,28
variable (mm) [Tfunnel (s)]
−0,5
(MPa)
model terms estimate
independent 290,80 0,551 74,144
V
w
/V
p
10,26 0,101 1,062
w/c 25,24 0,067 -4,774
Sp/p 15,39 0,013 0,959
V
s
/V
m
-11,97 -0,050 -1,058
(V
w
/V
p
)×(w/c) NS 0,009 -1,621
(V
w
/V
p
)×(V
s
/V
m
) NS -0,009 NS
(w/c)×(Sp/p) -2,77 NS NS
(V
w
/V
p
)
2
NS -0,005 -1,111
(w/c)
2
-5,08 -0,013 NS
(Sp/p)
2
NS NS -1,012
(V
s
/V
m
)
2
NS NS -0,744
residual error, ε
a
mean 0 0 0
standard deviation 2,998 0,010
b
1,607
R
2
0,991 0,993 0,909
R
2
adj
0,989 0,990 0,873
(NS) non-signiﬁcant terms;
a
error term is a random and normally distributed variable;
b
corresponding value for Tfunnel is 0,19
124
5.3. Collected data, ﬁtted models and mixtures optimization
Figure 5.9.: Comparison of measured versus predicted values of Dﬂow (CEM II/A-L
42,5 R)
response. The quadratic term in w/c was signiﬁcant for both Dﬂow and Tfunnel re-
sponses. The quadratic terms in Sp/p and V
s
/V
m
were also found to be signiﬁcant on
fc,28 response.
Contour plots representing the adjusted values of V
w
/V
p
and Sp/p for each pair of
(V
s
/V
m
; w/c ) in optimized mortars containing CEM II/A-L 42,5 R are presented in
Figure 5.12 (a). The corresponding contour plot for estimated mortar compressive
strength is presented in Figure 5.12 (b).
5.3.4. CEM II/B-L 32,5 N
The experimental plan corresponding to CEM II/B-L 32,5 N was carried out between
the 23
rd
and 25
th
of January 2006. A cement sample was taken from the ﬁrst delivery
of CEM II/B-L 32,5 N supplied by Cimpor, from the Alhandra production centre.
Statistics of the obtained results are presented in Table 5.11 for all data points and for
the central points only. The range of mortar properties covered with this experimental
plan was: Dﬂow ranging from 224 to 346 mm, Tfunnel ranging from 2 to 12 seconds and
fc,28 ranging from 47 to 80 MPa. The estimates of the ﬁtted models, including the resid-
ual error term, along with the correlation coeﬃcients, are given in Table 5.12. Again, the
estimated residual standard deviation (see Table 5.12) does not exceed the experimental
error by far (see Table 5.11). The measured-to-predicted values of Dﬂow, Tfunnel and
fc,28 are shown in Figures 5.13, 5.14 and 5.15, respectively, with the prediction inter-
vals corresponding to a 95% conﬁdence level. The ratio between predicted-to-measured
values (central points and additional data) for Dﬂow, Tfunnel and fc,28 ranged between
125
5. Optimization of SCC mortar mixtures
Figure 5.10.: Comparison of measured versus predicted values of Tfunnel (CEM II/A-L
42,5 R)
Figure 5.11.: Comparison of measured versus predicted values of fc,28 (CEM II/A-L
42,5 R)
126
5.3. Collected data, ﬁtted models and mixtures optimization
(a) (b)
Figure 5.12.: Optimized mortars incorporating CEM II/A-L 42,5 R (a) range of mixture
variables and (b) estimated values of fc,28 (MPa)
0,97 and 1,01, 0,94 and 1,19, 0,99 and 1,07, respectively.
The results in Table 5.15 again show that w/c exhibit a great eﬀect on all three measured
responses, being only exceeded by the eﬀect of V
w
/V
p
on (Tfunnel
−0,5
) response. Besides
w/c the variable that most inﬂuenced Dﬂow was Sp/p. Signiﬁcant interaction eﬀects
were found between V
w
/V
p
and Sp/p and between w/c and V
s
/V
m
on Dﬂow response.
Signiﬁcant interaction eﬀects were found between V
w
/V
p
and w/c, V
w
/V
p
and V
s
/V
m
and
between Sp/p and V
s
/V
m
on (Tfunnel
−0,5
) response. The quadratic term in w/c was
signiﬁcant for both Dﬂow and fc,28 responses.
Contour plots representing the adjusted values of V
w
/V
p
and Sp/p for each pair of
(V
s
/V
m
; w/c) and a contour plot for estimated mortar compressive strength are presented
Table 5.11.: Statistics of the results for the total points and for central points (CEM
II/B-L 32,5 N)
N=28 total points n
c
=4 central points
Dﬂow Tfunnel fc,28 Dﬂow Tfunnel fc,28
(mm) (s) (MPa) (mm) (s) (MPa)
Minimum 224 1,9 47,3 295 4,1 59,7
Maximum 345 12,5 80,3 304 4,71 61,2
Mean 293 5,3 61,2 299 4,4 60,5
Standard deviation 28 2,9 7,9 4 0,3 0,8
Coeﬃcient of variation 9,6% 54,0% 12,9% 1,3% 6,6% 1,2%
127
5. Optimization of SCC mortar mixtures
Table 5.12.: Fitted numerical models for coded variables (CEM II/B-L 32,5 N)
Response Dﬂow fc,28
variable (mm) [Tfunnel (s)]
−0,5
(MPa)
model terms estimate
independent 295,12 0,476 60,406
V
w
/V
p
10,30 0,103 -0,719
w/c 18,05 0,050 -8,057
Sp/p 17,39 0,019 0,675
V
s
/V
m
-11,05 -0,045 NS
(V
w
/V
p
)×(w/c) NS 0,008 NS
(V
w
/V
p
)×(Sp/p) -2,30 NS NS
(V
w
/V
p
)×(V
s
/V
m
) NS -0,010 NS
(w/c)×(V
s
/V
m
) 2,61 NS NS
(Sp/p)×(V
s
/V
m
) NS -0,006 NS
(w/c)
2
-2,56 NS 0,872
residual error, ε
a
mean 0 0 0
standard deviation 4,379 0,010
b
1,684
R
2
0,976 0,992 0,954
R
2
adj
0,967 0,990 0,946
(NS) non-signiﬁcant terms;
a
error term is a random and normally distributed variable;
b
corresponding value for Tfunnel is 0,41
Figure 5.13.: Comparison of measured versus predicted values of Dﬂow (CEM II/B-
L 32,5 N)
128
5.3. Collected data, ﬁtted models and mixtures optimization
Figure 5.14.: Comparison of measured versus predicted values of Tfunnel (CEM II/B-
L 32,5 N)
Figure 5.15.: Comparison of measured versus predicted values of fc,28 (CEM II/B-L 32,5
N)
129
5. Optimization of SCC mortar mixtures
(a) (b)
Figure 5.16.: Optimized mortars incorporating CEM II/B-L 32,5 N (a) range of mixture
variables and (b) estimated values of fc,28 (MPa)
in Figure 5.16 (a) and (b), respectively, corresponding to optimized mortars containing
CEM II/B-L 32,5 N. In the case of this cement type and the analyzed region, the
contour lines for estimated mortar compressive strength are nearly horizontal due to
the dominant eﬀect of w/c in this response.
5.3.5. CEM IV/B (V) 32,5 N
The experimental plan corresponding to CEM IV/B(V) 32,5 N was carried out between
the 25
th
and 30
th
of January 2006. A cement sample was taken from the ﬁrst delivery
of CEM IV/B(V) 32,5 N supplied by Cimpor, from the Alhandra production centre.
Statistics of the obtained results are presented in Table 5.13 for all data points and for
the central points only. The range of mortar properties covered with this experimental
plan was: Dﬂow ranging from 185 to 360 mm, Tfunnel ranging from 3 to 18 s and fc,28
ranging from 47 to 67 MPa. In this case the highest experimental error is associated
with the fc,28 response instead of TFunnel response variable. The estimates of the
ﬁtted models, including the residual error term, along with the correlation coeﬃcients,
are given in Table 5.14. The estimated residual standard deviation (see Table 5.14)
does not exceed the experimental error (see Table 5.13) except in the case of Tfunnel
response, but the experimental error associated to Tfunnel was relatively low compared
to previous experimental plans. The measured-to-predicted values of Dﬂow, Tfunnel
and fc,28 are shown in Figures 5.17, 5.18 and 5.19, respectively, with the prediction
130
5.3. Collected data, ﬁtted models and mixtures optimization
Table 5.13.: Statistics of the results for the total points and for central points (CEM
IV/B(V) 32,5 N)
N=28 total points n
c
=4 central points
Dﬂow Tfunnel fc,28 Dﬂow Tfunnel fc,28
(mm) (s) (MPa) (mm) (s) (MPa)
Minimum 185 2,8 46,9 300 5,8 48,7
Maximum 360 18,0 66,6 312 6,1 57,0
Mean 290 7,4 57,0 308 6,0 53,9
Standard deviation 46 3,8 5,5 5 0,1 3,6
Coeﬃcient of variation 15,8% 52,3% 9,6% 1,8% 2,3% 6,7%
Figure 5.17.: Comparison of measured versus predicted values of Dﬂow (CEM IV/B(V)
32,5 N)
intervals corresponding to a 95% conﬁdence level. The ratio between predicted-to-
measured values (central points and additional data) for Dﬂow, Tfunnel and fc,28 ranged
between 0,94 and 1,13, 0,87 and 1,19, 0,96 and 1,12 , respectively.
The results in Table 5.14 show that w/c exhibit a great eﬀect on all three measured
responses, being only exceeded by the eﬀect of V
w
/V
p
on (Tfunnel
−0,5
) response. Besides
w/c, the variable that most inﬂuenced Dﬂow was Sp/p. A signiﬁcant interaction eﬀect
was found between w/c and Sp/p on Dﬂow response. Signiﬁcant interaction eﬀects were
found between V
w
/V
p
and w/c, V
w
/V
p
and V
s
/V
m
and w/c and V
s
/V
m
on (Tfunnel
−0,5
).
Only the interaction eﬀect between V
w
/V
p
and Sp/p was found to be signiﬁcant on fc,28
response. The quadratic term on V
w
/V
p
was signiﬁcant for all responses. The quadratic
term on w/c was signiﬁcant for both Dﬂow and Tfunnel responses. The quadratic term
on Sp/p was found to be signiﬁcant for both Dﬂow and fc,28. The quadratic term on
V
s
/V
m
was found to be signiﬁcant for fc,28 response only.
131
5. Optimization of SCC mortar mixtures
Table 5.14.: Fitted numerical models for coded variables (CEM IV/B(V) 32,5 N)
Response Dﬂow fc,28
variable (mm) [Tfunnel (s)]
−0,5
(MPa)
model terms estimate
independent 305,53 0,404 54,523
V
w
/V
p
12,40 0,080 -0,626
w/c 37,73 0,055 -4,438
Sp/p 21,50 0,013 2,477
V
s
/V
m
-10,65 -0,027 -1,020
(V
w
/V
p
)×(w/c) NS 0,013 NS
(V
w
/V
p
)×(Sp/p) NS NS 1,131
(V
w
/V
p
)×(V
s
/V
m
) NS -0,006 NS
(w/c)×(Sp/p) -8,97 NS NS
(w/c)×(V
s
/V
m
) NS -0,007 NS
(V
w
/V
p
)
2
-3,55 0,003 0,959
(w/c)
2
-10,52 -0,005 NS
(Sp/p)
2
-3,95 NS 0,994
(V
s
/V
m
)
2
NS NS 0,974
residual error, ε
a
mean 0 0 0
standard deviation 4,453 0,008
b
1,814
R
2
0,991 0,993 0,891
R
2
adj
0,987 0,989 0,845
(NS) non-signiﬁcant terms;
a
error term is a random and normally distributed variable;
b
corresponding value for Tfunnel is 0,53
Figure 5.18.: Comparison of measured versus predicted values of Tfunnel (CEM
IV/B(V) 32,5 N)
132
5.3. Collected data, ﬁtted models and mixtures optimization
Figure 5.19.: Comparison of measured versus predicted values of fc,28 (CEM IV/B(V)
32,5 N)
Contour plots representing the adjusted values of V
w
/V
p
and Sp/p for each pair of
(V
s
/V
m
; w/c) in optimized mortars containing CEM IV/B(V) 32,5 N are presented
in Figure 5.20 (a). The corresponding contour plot for estimated mortar compressive
strength is presented in Figure 5.20 (b). By observing Figure 5.20 (b) a larger curvature
is found in the contour lines compared to previous contour plots. This is in part due to
the wider scale of variables in the x- and y-axis but also a change in cement type.
5.3.6. CEM II/B-L 32,5 R (BR)
The experimental plan corresponding to CEM II/B-L 32,5 R (BR), white cement, was
carried out between the 18
th
and 20
th
of January 2006. A cement sample was taken
from the ﬁrst delivery of CEM II/B-L 32,5 R (BR) supplied by Cimpor, but produced
by Secil.
Statistics of the obtained results are presented in Table 5.15 for all data points and for the
central points only. The range of mortar properties covered with this experimental plan
was: Dﬂow ranging from 184 to 340 mm, Tfunnel ranging from 2 to 22 s and fc,28 ranging
from 52 to 87 MPa. From the central points data it can be concluded that the highest
experimental error is associated with the fc,28 response variable. The estimates of the
ﬁtted models, including the residual error term, along with the correlation coeﬃcients,
are given in Table 5.16. The estimated residual standard deviation (see Table 5.16) does
not exceed the experimental error by far (see Table 5.15). The measured-to-predicted
values of Dﬂow, Tfunnel and fc,28 are shown in Figures 5.21, 5.22 and 5.23, respectively,
with the prediction intervals corresponding to a 95% conﬁdence level. The ratio between
133
5. Optimization of SCC mortar mixtures
(a) (b)
Figure 5.20.: Optimized mortars incorporating CEM IV/B(V) 32,5 N (a) range of mix-
ture variables and (b) estimated values of fc,28 (MPa)
predicted-to-measured values (central points and additional data) for Dﬂow, Tfunnel
and fc,28 ranged between 0,95 and 1,04, 0,87 and 1,19, 0,95 and 1,22 , respectively.
The results in Table 5.16 show that instead of w/c, Sp/p is the variable that most
inﬂuenced Dﬂow of mortar mixtures incorporating CEM II/B-L 32,5 R (BR). Besides
Sp/p the variable that most inﬂuenced Dﬂow was V
w
/V
p
. V
w
/V
p
and w/c exhibited
the greatest eﬀect on (Tfunnel
−0,5
) and fc,28 responses, respectively. Signiﬁcant inter-
action eﬀects were found between w/c and Sp/p for all response variables. Signiﬁcant
interaction eﬀects were found between V
w
/V
p
and w/c for both (Tfunnel
−0,5
) and fc,28
responses. The interaction eﬀect between V
w
/V
p
and Sp/p was also found signiﬁcant for
both Dﬂow and fc,28 responses. The interaction eﬀect between V
w
/V
p
and V
s
/V
m
was
Table 5.15.: Statistics of the results for the total points and for central points (CEM
II/B-L 32,5 R (BR))
N=28 total points n
c
=4 central points
Dﬂow Tfunnel fc,28 Dﬂow Tfunnel fc,28
(mm) (s) (MPa) (mm) (s) (MPa)
Minimum 184 1,89 52,1 308 5,72 66,6
Maximum 340 21,5 87,1 318 6,0 72,4
Mean 295 7,6 69,0 314 5,9 69,2
Standard deviation 36 4,7 9,0 5 0,2 2,4
Coeﬃcient of variation 12,3% 62,0% 13,1% 1,6 2,6% 3,5%
134
5.3. Collected data, ﬁtted models and mixtures optimization
Table 5.16.: Fitted numerical models for coded variables (CEM II/B-L 32,5 R (BR))
Response Dﬂow fc,28
variable (mm) [Tfunnel (s)]
−0,5
(MPa)
model terms estimate
independent 309,82 0,40639 68,98
V
w
/V
p
12,89 0,09618 -0,18
w/c 7,75 0,03239 -7,84
Sp/p 23,11 0,02087 4,16
V
s
/V
m
-9,44 -0,02918 NS
(V
w
/V
p
)×(w/c) NS 0,01211 2,07
(V
w
/V
p
)×(Sp/p) -4,84 NS 1,37
(V
w
/V
p
)×(V
s
/V
m
) NS -0,00688 NS
(w/c)×(Sp/p) -5,78 -0,00671 -1,46
(V
w
/V
p
)
2
-10,10 NS NS
(Sp/p)
2
-5,97 NS NS
residual error, ε
a
mean 0 0 0
standard deviation 5,487 0,01028
b
2,63
R
2
0,966 0,989 0,918
R
2
adj
0,950 0,985 0,893
(NS) non-signiﬁcant terms;
a
error term is a random and normally distributed variable;
b
corresponding value for Tfunnel is 0,53
135
5. Optimization of SCC mortar mixtures
Figure 5.21.: Comparison of measured versus predicted values of Dﬂow (CEM II/B-L
32,5 R (BR))
signiﬁcant for (Tfunnel
−0,5
) response. Signiﬁcant quadratic terms on V
w
/V
p
and Sp/p
were found when modelling Dﬂow response.
Contour plots representing the adjusted values of V
w
/V
p
and Sp/p for each pair of
(V
s
/V
m
; w/c) in optimized mortars containing CEM II/B-L 32,5 R (BR) are presented
in Figure 5.24 (a). The corresponding contour plot for estimated mortar compressive
strength is presented in Figure 5.24 (b).
5.4. Discussion of results
5.4.1. Main eﬀects
In general, the main eﬀects found in the models obtained for Dﬂow, (Tfunnel
−0,5
) and
fc,28 were the ﬁrst order eﬀects for all cement types. Considering only the ﬁrst order
eﬀects, it was found that Dﬂow increases with V
w
/V
p
, w/c and Sp/p and decreases with
an increase of V
s
/V
m
, independently of the cement type, as found by Okamura et al.
(2000). On the contrary, Tfunnel increases with V
s
/V
m
and decreases with an increase
of V
w
/V
p
, w/c or Sp/p, independently of the cement type, as also found by Okamura
et al. (2000). As expected, fc,28 decreased with an increase of w/c or V
w
/V
p
, for all
cement types. Moreover, it was found that fc,28 increases with Sp/p for all cement
types, except in the case of CEM I 52,5 R. The inﬂuence of V
s
/V
m
on fc,28 response
changed with the cement type.
The variables that inﬂuenced Dﬂow the most were w/c followed by Sp/p for all cement
types, except CEM II/B-L 32,5 R (BR). In the case of this cement type, Dﬂow was more
136
5.4. Discussion of results
Figure 5.22.: Comparison of measured versus predicted values of Tfunnel (CEM II/B-L
32,5 R (BR))
Figure 5.23.: Comparison of measured versus predicted values of fc,28 (CEM II/B-L 32,5
R (BR))
137
5. Optimization of SCC mortar mixtures
(a) (b)
Figure 5.24.: Optimized mortars incorporating CEM II/B-L 32,5 R (BR) (a) range of
mixture variables and (b) estimated values of fc,28 (MPa)
inﬂuenced by Sp/p followed by V
w
/V
p
. The variables that inﬂuenced (Tfunnel
−0,5
) the
most were V
w
/V
p
followed by w/c , independent of the cement type. w/c was also found
to be the most signiﬁcant variable to explain fc,28 response, independent of the cement
type.
Diﬀerent interaction eﬀects and other quadratic terms were found to be signiﬁcant for
the mortar responses, depending on the cement type. The quadratic term in w/c was
signiﬁcant for Dﬂow response of all cement types, except CEM II/B-L 32,5 R (BR).
5.4.2. Interaction diagrams and number of optimized solutions
The mixture parameters of optimized solutions can be represented in an interaction
diagram like the one presented in Figure 5.25 as an alternative to the contour plots
presented before. In this diagram all mixture parameters are presented in coded values
and each circle corresponds to a level of the mixture variable. Each radial segment
represents one of the optimized solutions and over this segment a dot is plotted for
each mixture variable, using the correspondent marker. The distance of each dot to the
centre is determined by the variable coded value. The interaction diagrams of the type
presented in Figure 5.25 show a more global picture of the solutions highlighting other
features of the optimization since they enable one:
• to rapidly distinguish which of the constituent materials combine well, in the sense
that they favor the occurrence of a large range of optimized solutions, that can
138
5.4. Discussion of results
be seen in the diagram by the emergence of a large amount of radial lines in the
circle;
• to distinguish regions where the solutions get very close to the circular boundaries
corresponding to levels -2 and +2, indicating that the limits of the factorial plan
have been attained and one has depleted the set of solutions;
• to identify trends in V
w
/V
p
, w/c and Sp/p for a given constant level of V
s
/V
m
, by
selecting successive arcs of ﬁlled dots and by following other marked curves in the
corresponding sector;
• to identify situations of total or large incompatibility between cement and super-
plasticizer, in which case other superplasticizers should be tested.
By analyzing the diagrams in Figure 5.25 a common pattern is found for all cement
types. In fact, both V
w
/V
p
and Sp/p increase when w/c is reduced for a given aggregate
content (V
s
/V
m
), to maintain self-compactability properties. On the other hand, both
Sp/p and V
w
/V
p
increase with aggregate content for a constant w/c ratio. The number
of solutions found depends not only on the selected central point in each experimental
plan but also on the interaction of the speciﬁc cement type with the limestone ﬁller
and the V3000 superplasticizer. This means that a diﬀerent number of solutions can
be found by changing the value a
0
for one or more of the independent variables. In
the particular cases of CEM I 42,5 R, CEM I 52,5 R and CEM II/A-L 42,5 R (see
Figure 5.25 (a) to (c)) one of the variables that clearly limited the number of solutions
found was V
w
/V
p
, which reached a -2 value for each level of V
s
/V
m
. Thus, one can expect
to ﬁnd more solutions for higher values of w/c by reducing the absolute value of V
w
/V
p
,
corresponding to the central point. CEM II/B-L 32,5 R (BR) can be distinguished by
its very low values and small variation of Sp/p across the solutions range as compared to
other cements (see Figure 5.24 (a) and Figure 5.25 (f)). This can be explained mainly by
the diﬀerences in the chemical composition, namely, lower content of Al
2
O
3
and Fe
2
O
3
and lower alkalis content (see Table C.7 in Appendix C) which overcame the negative
eﬀect of a higher speciﬁc surface on ﬂuidity (as discussed in Chapter 3).
5.4.3. Inﬂuence of cement type on mix proportions of SCC mortars
By analyzing only the solutions found for V
s
/V
m
=0,45, the main diﬀerences between
diﬀerent cement types are highlighted in Figures 5.26 to 5.28. In general, it can be ob-
served from Figure 5.26 that the optimum superplasticizer dosage signiﬁcantly changes
with the cement type for each w/c ratio. CEM I 52,5 R required the highest dosage
while a strong reduction in Sp/p was observed for CEM II/B-L 32,5 R (BR). Based on
literature review (see Chapter 3), the required superplasticizer (PC type) dosage may
139
5. Optimization of SCC mortar mixtures
Figure 5.25.: Range of optimized mixture variables (coded values) for diﬀerent cement
types
140
5.4. Discussion of results
Figure 5.26.: Variation of Sp/p with w/c for optimized mortars with V
s
/V
m
=0,45
increase with C
3
A content, surface area and alkali sulphate content in cement. Further-
more, the substitution of cement by limestone ﬁller, until a certain dosage, reduces the
required superplasticizer dosage. Unfortunately, one failed to obtain a complete char-
acterization of cements for the ﬁrst delivery (see Tables C.1 to C.10, in Appendix C)
therefore a further discussion of the inﬂuence of cement characteristics on the required
superplasticizer dosage can not be given here.
The values found for V
w
/V
p
are relatively close to each other for all cement types except
for CEM II/B-L 32,5 R (BR) (see Figure 5.27). In general, values lower than the typical
range of V
w
/V
p
suggested in (BIBM et al., 2005) [0,85; 1,10], were found to be adequate
for the six Portuguese cement types tested in combination with superplasticizer V3000
and limestone ﬁller. The estimated values of fc,28 (MPa) varied almost linearly with
w/c but can diﬀer signiﬁcantly depending on the cement type, for a given w/c ratio
(see Figure 5.28). This can be explained by diﬀerent amounts and type of additions
incorporated in each cement. As expected CEM I 52,5 R and CEM IV/B(V) 32,5 N led
to the highest and the lowest compressive strength (28 days), respectively, for a given
w/c ratio. The correction of w/c ratio to attain a certain target level of compressive
strength with diﬀerent cement types can also be estimated directly from Figure 5.28,
with the possible exception of CEM I 52,5 (the ﬁtted model explained only 58% of the
variation in the response).
5.4.4. Quality control
The optimized mixtures presented above (incorporating reference sand) are useful to
assess the workability and strength variations in diﬀerent deliveries of cement, ﬁller or
141
5. Optimization of SCC mortar mixtures
Figure 5.27.: Variation of V
w
/V
p
with w/c for optimized mortars with V
s
/V
m
=0,45
Figure 5.28.: Variation of estimated values of fc,28 (MPa) for optimized mortars with
V
s
/V
m
=0,45
142
5.4. Discussion of results
superplasticizer. A quality control plan should be established to monitor the extent
to which optimized mortar properties meet speciﬁcations. The elimination of excessive
deviations from target speciﬁcations and excessive variability around target speciﬁca-
tions will contribute to enhance the robustness of SCC production process (BIBM et al.,
2005). This procedure was adopted to assess the inﬂuence of diﬀerent deliveries of ce-
ment on the workability and strength properties of SCC mortars and pastes, and is
explained in Chapter 6.
5.4.5. Predicting capability of the models
According to Sonebi et al. (2005); Yahia and Khayat (2001b) the derived models can
still be used to predict mortar properties when the material properties changes, such as
cement source. To evaluate the eﬀect of cement source on the accuracy of the models,
mortars incorporating three new cements of type CEM I 42,5 R from a diﬀerent factory
or supplier, and maintaining the other constituent materials, were assessed. The mix
proportions and test results of the 28 mixes prepared for the CEM I 42,5 R produced
by Secil-Maceira, Secil-Outão and Cimpor-Souselas are summarized in Tables E.7, E.8
and E.9, respectively, in Appendix E. The scatter between the measured and the pre-
dicted (using the models derived to CEM I 42,5 R, Cimpor–Alhandra, presented in
paragraph 5.3.1) values of mortar properties can be observed in Figures 5.29 to 5.31.
From these ﬁgures it can be concluded that the models derived in section 5.3.1 are no
longer adequate if the cement source is changed; and adjustments in the mixture param-
eters are required to maintain the mortar properties. But, since the relative inﬂuence of
each variable on mortar responses do not signiﬁcantly change with changes in material
properties (as pointed in paragraphs 5.4.1 and 5.4.2) a limited number of tests should
be enough to adjust mixture parameters.
5.4.6. Cement/superplasticizer combination
For both economical and technical reasons (as it will be demonstrated in Chapter 7) the
combination of cement and superplasticizer which leads to lower dosages of superplas-
ticizer should be preferred. From this point of view the combination of CEM I 52,5 R
with V3000 was the least eﬃcient from all the combinations studied in this work (see
Figure 5.25). In cases like this, or even in cases of total incompatibility (a zero number
of optimum solutions), other superplasticizers should be tested. By only changing the
superplasticizer type from the same supplier, Viscocrete 3005 (V3005), a larger number
of solutions could be found for mortars incorporating CEM I 52,5 R and for a wider
range of superplasticizer dosages, as can be observed in Figures 5.32. V3005 is also a
polycarboxylate type superplasticizer having a speciﬁc gravity of 1,05 and 25,5% solid
143
5. Optimization of SCC mortar mixtures
Figure 5.29.: Measured versus predicted values of Dﬂow for CEM I 42,5 R from diﬀerent
sources
Figure 5.30.: Measured versus predicted values of Tfunnel for CEM I 42,5 R from dif-
ferent sources
144
5.5. Concluding remarks
Figure 5.31.: Measured versus predicted values of fc,28 for CEM I 42,5 R from diﬀerent
sources
content. The mix proportions and test results of the 28 mixes prepared for CEM I 52,5 R
(Cimpor-Alhandra) in combination with this superplasticizer (V3005), limestone ﬁller
and reference sand are summarized in Table E.10 of Appendix E.
The approximation of the Sp/p contour lines in Figure 5.32, which can also be clearly
observed in Figure 5.4 (a) in the case of V3000, indicates the saturation of superplas-
ticizer’s eﬀect. Based on these ﬁgures, it can be concluded that the saturation dosage
(Sp/p) of V3005 and V3000 superplasticizer types is around 1,1% and 2,2%, respectively.
5.5. Concluding remarks
In this chapter a comprehensive procedure for the design of mortar mixtures which
are adequate for SCC is provided. Six diﬀerent types of cement, currently used by the
Portuguese construction industry, were assessed in combination with limestone ﬁller and
a polycarboxylate type superplasticizer.
An experimental plan conducted according to a central composite design is useful to eval-
uate the eﬀects of mixture parameters and their interactions on SCC mortar properties
while reducing the number of trial batches needed to achieve balance among mixture
variables. The mixture variables considered in the Japanese-method (Okamura et al.,
2000) can be used as factors in the factorial design to characterize the behaviour of SCC
mortar mixtures.
For a given combination of cement + limestone ﬁller + superplasticizer (V3000) a large
number of solutions could be found that lead to a spread ﬂow of 260 mm and a ﬂow
145
5. Optimization of SCC mortar mixtures
(a)
(b)
Figure 5.32.: Range of optimized mixture variables for the combination of
CEM I 52,5 R+ limestone ﬁller+V3005: (a) contour plot of absolute values
and (b) interaction diagram with coded values
146
5.5. Concluding remarks
time of 10 s, in a wide range of w/c ratio and ﬁne aggregate content. The number of
solutions that were found depends on the interaction between constituent materials but
also on the selected range of mixture parameters for the experimental plan.
Each type of cement has unique properties (physical and chemical) that will interact
with other constituents, especially additions and admixtures, which reﬂects on the range
of mixture levels of optimized mortar mixtures. Contour plots and interaction diagrams
were suggested to represent the range of mixture levels where optimum solutions can be
found. Values lower than the typical range of V
w
/V
p
suggested in (BIBM et al., 2005)
[0,85; 1,10], were found to be adequate for the six Portuguese cement types tested in
combination with superplasticizer (V3000) and limestone ﬁller.
Information from the contour plots or interaction diagrams can simplify the test protocol
required to optimize a given SCC mixture, namely, to select the combination of powder
materials with admixtures. In particular, they are useful to compare the eﬃciency of
diﬀerent admixtures (superplasticizers, viscosity agents, or combination of both) and
alternative additions. Optimized mortar mixtures can serve as reference mixtures in
a quality control plan to detect variations in diﬀerent deliveries of cement, ﬁller or
superplasticizer. The derived models can still be useful to predict mortar properties
when the material properties change, such as the cement source.
The procedure presented in this chapter can easily be implemented in any concrete
laboratory of a production centre since it involves mortar tests which are easy to carry
out and involves simple and easy to construct test equipments. The variables considered
in this work have by no means exhausted the range of factors that aﬀect SCC mortar
properties. For example, changes in environmental conditions like temperature and
humidity, the evolution of properties over time, the mixing energy and stress history
until the time of application, the incorporation of other additions or admixtures, can
signiﬁcantly change mortar properties, in both fresh and hardened states.
147
5. Optimization of SCC mortar mixtures
148
6. Inﬂuence of cement variations on
SCC mortar/paste properties
6.1. Introduction
This chapter deals with the inﬂuence of diﬀerent production dates of cement on fresh and
hardened properties of mortar/paste mixes. The three main objectives of the current
study are: ﬁrst, to assess how large the ﬂuctuations on fresh mortar/paste properties
can become when a new delivery of cement is used; second, to identify the constituents
in cement which might have caused the ﬂuctuations in mortar/paste properties and,
third, to verify if the diﬀerent test results obtained on pastes correlate with each other,
in particular, empirical and rheological test results.
6.2. Cement production quality control
6.2.1. Importance of cement variations for SCC robustness
According to BIBM et al. (2005) all cements which conform to EN 197-1 can be used for
production of SCC. Generally, cement is seen as one of the constituent materials of con-
crete with less variation due to a stringent quality control during production. However,
recent studies have shown that cement variations have a greater eﬀect on workability
and on early reactions of concrete than is generally thought (Juvas et al., xxxx; Wallevik
et al., 2007). Kubens and Wallevik (2006) found that the eﬀect of the production date
of cement on rheology was only slight in mixtures without admixtures but more promi-
nent in mixes containing dispersing admixtures. Yield stress results (or more precisely,
G-yield result obtained from Con Tec Rheomixer) of blank, polycarboxylate ether and
melamine mixes (7 samples), containing CEM I 52,5 R, had a coeﬃcient of variation
of 15%, 44% and 39%, respectively. These authors observed a similar trend for mixes
containing other cements (three CEM I 42,5 R from diﬀerent producers, and one CEM
II/B-S 32,5 R) (Kubens and Wallevik, 2006). Juvas et al. (xxxx) found similar results
(see Figure 6.1) when comparing the spread ﬂow results of mortars without admixture,
149
6. Inﬂuence of cement variations on SCC mortar/paste properties
Figure 6.1.: Flow table test results (Juvas et al., xxxx)
containing a typical melamine plasticizer (SP) and a polycarboxylate plasticizer (SSP),
from 50 samples of daily collected CEM I 52,5 R cement type. In Figure 6.1 larger
variations are clearly observed with mixtures containing admixtures when compared to
plain mixtures.
The rheology of plain mortar mixtures, with a water/cement ratio of about 0,50, is
controlled largely by water (Aïtcin et al., 2001). In these mixtures, the cement grains
are not uniformly dispersed throughout the water but tend to form small ﬂocs which
trap water within them (see Figure 6.2 (a)). By adding water these cement ﬂocs are
kept apart as a means of inﬂuencing the rheology of the paste (Aïtcin et al., 2001). Later
in time, the ﬁrst hydration products begin to interfere with the unrestricted movement
of the cement ﬂocs. Dispersing admixtures are used to both break up the ﬂocs and to
maintain the dispersion in a way that causes the cement particles to distribute more
uniformly throughout the aqueous phase, reducing the yield stress value for a given water
content and so increasing the ﬂuidity of the mix. Not only has the ﬂuidity increased,
but more sites on the surface of the cement grains are available for interaction with
superplasticizer and hydration (Dransﬁeld, 2003). If water is removed from the system
lowering the water/cement ratio to 0,3∼0,4, like in the case of mixtures mentioned above
(Juvas et al., xxxx; Wallevik et al., 2007), the ﬂuidity can be reduced back to what it was
before the admixture addition. However, the average inter-grain distance will also reduce
(see Figure 6.2 (b)), so less hydration will be necessary before the space between the
cement grains is ﬁlled with hydration product to give setting and strength development
(Dransﬁeld, 2003). Finally, there will be less void space, not ﬁlled with any hydration
product, resulting in fewer capillaries and therefore improved ﬁnal strength and better
durability (Dransﬁeld, 2003). In conclusion, the mixtures containing superplasticizers
are more complex systems and at low w/c ratios a small diﬀerence in the dispersion
150
6.2. Cement production quality control
(a)
(b)
Figure 6.2.: (a) Flocculated cement particles and (b) deﬂocculated cement particles after
superplasticizer addition
eﬀect of the admixture changes the ﬂuidity remarkably.
Wallevik et al. (2007) reported that the ﬂuctuations observed in mortar, due to ce-
ment variations, can also be observed at the concrete level. It is suggested that the
inﬂuence of cement variations on concrete properties can be more or less pronounced
depending on cement content in the concrete mixture (Wallevik et al., 2007). In the
case of self-compacting concrete, a high-range water reducer
1
must be incorporated and
often mixtures have a higher content of cement when compared to conventional concrete
mixtures. For these reasons, cement variations have became an important factor when
discussing the robustness of SCC production.
6.2.2. Control of cement properties
The control of cement properties by the cement manufacturers is achieved ﬁrst by control
of raw mix chemistry, ﬁneness and homogeneity; second, by control of clinker chemistry
and degree of heat treatment and, ﬁnally, by control of cement ﬁneness, SO
3
level and
forms of SO
3
present (Newman and Choo, 2003). In general, the cement properties
of greatest importance for customers are strength at 28 days, early strength, water
demand, strength growth and alkali content. Besides, other aspects like interaction with
admixtures and/or additions, heat of hydration, colour and ﬂowability can be important
to some particular applications. But above all, it is the consistency of performance in
relation to these aspects that is of greatest importance to costumers (Newman and Choo,
2003).
The current Portuguese standard for common cements is NP EN 197-1 (Portugal. IPQ,
2001). In Table 6.1 the requirements imposed by NP EN 197-1 for the cement types
used in this thesis (and most commonly used in Portugal) are summarized. Besides
these requirements, NP EN 197-1 deﬁnes the permitted chemical composition of the
individual constituents, the nature of minor additional constituents and the permitted
1
Polycarboxylate type superplasticizers are the most used.
151
6. Inﬂuence of cement variations on SCC mortar/paste properties
level of additives. NP EN 197-1 also describes the testing frequencies and the method
of data analysis required to demonstrate compliance with EN 197-1.
None of the standard tests on cement (like setting or water demand) incorporate chem-
ical admixtures, so these quality control results cannot give any information about
cement/superplasticizer interaction (Wallevik et al., 2007). Moreover, Blaine ﬁneness
(the most observed single property of cement) is not a suﬃcient parameter for explain-
ing the variations in the properties of fresh concrete mix or early age properties of
concrete, especially with mixtures containing superplasticizers (Erdogdu, 2000; Juvas
et al., xxxx; Kim et al., 2000). The two most commonly employed tests for study-
ing cement/superplasticizer interaction are grout-based tests, namely, the mini-cone
test (Aïtcin et al., 2001; Gomes, 2002; Roussel, 2006b; Roussel and Coussot, 2005;
Schwartzentruber et al., 2006) and the Marsh ﬂow test (de Larrard et al., 1997; Aïtcin
et al., 2001; Gomes, 2002; Roussel and Le Roy, 2005; Schwartzentruber et al., 2006).
These grout-based tests are preferred to concrete tests because they are less demanding
in terms of materials, energy, time and space. Juvas et al. (xxxx) suggested the use
of the ﬂow table test described in Punkki and Penttala (xxxx), and the semiadiabatic
calorimeter to measure the uniformity of cement properties concerning workability and
heat release at plant conditions. In order to get more accurate information from rheo-
logical properties a mortar/concrete viscometer (Kubens and Wallevik, 2006; Wallevik
et al., 2007) or a paste rheometer can be used. However, these types of tests may not
be suitable for the factory environment or construction site.
6.2.3. Inﬂuence of cement parameters on concrete properties of
mixtures without admixtures
The standard cement properties of water demand (workability), setting time and strength
development are determined mainly by (Newman and Choo, 2003):
• cement ﬁneness (surface area and residue at 45 µm);
• loss on ignition (LOI);
• clinker alkalis and SO
3
;
• clinker free lime;
• clinker compound composition (mainly, C
3
S and C
3
A );
• cement SO
3
and the forms of SO
3
present.
152
6.2. Cement production quality control
T
a
b
l
e
6
.
1
.
:
R
e
q
u
i
r
e
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e
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e
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t
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b
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-
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(
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.
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M
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4
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,
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(
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)
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5
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,
5
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(
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l
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(
%
)
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-
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8
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4
6
5
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9
L
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t
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l
l
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,
L
(
%
)
–
–
6
-
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0
2
1
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3
5
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1
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3
5
S
i
l
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s
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y
a
s
h
,
V
(
%
)
–
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–
–
3
5
-
5
5
–
M
i
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o
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a
d
d
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.
c
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s
t
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.
(
%
)
0
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,
7
d
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6
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f
c
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8
d
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s
(
M
P
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,
5
≥
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t
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(
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≥
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i
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m
m
)
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≤
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≤
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1
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C
h
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m
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:
L
o
s
s
o
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i
g
n
i
t
i
o
n
(
%
)
≤
5
≤
5
–
–
–
–
I
n
s
o
l
u
b
l
e
r
e
s
i
d
u
e
(
%
)
≤
5
≤
5
–
–
–
–
S
u
l
p
h
a
t
e
,
a
s
S
O
3
(
%
)
≤
4
≤
4
≤
4
≤
3
,
5
≤
3
,
5
≤
3
,
5
C
h
l
o
r
i
d
e
(
%
)
≤
0
,
1
≤
0
,
1
≤
0
,
1
≤
0
,
1
≤
0
,
1
≤
0
,
1
P
o
z
z
o
l
a
n
i
c
i
t
y
–
–
–
–
s
a
t
i
s
ﬁ
e
s
t
h
e
t
e
s
t
–
M
g
O
≤
5
%
i
n
c
l
i
n
k
e
r
a
s
p
e
c
i
ﬁ
e
d
i
n
t
e
r
m
s
o
f
c
h
a
r
a
c
t
e
r
i
s
t
i
c
v
a
l
u
e
s
153
6. Inﬂuence of cement variations on SCC mortar/paste properties
Workability (water demand)
The key parameters to ensure satisfactory workability characteristics of mixtures with-
out admixtures are an appropriate ratio of SO
3
to alkalis in the clinker and an appropri-
ate level of dehydrated gypsum in the cement (Newman and Choo, 2003). Nevertheless,
particle size distribution and speciﬁc surface area of cement also aﬀect water demand
(Hewlett, 2004).
The crystallization products formed after cooling of the clinker liquid depend on the
relative levels of the two alkali oxides (Na
2
O and K
2
O) and the level of SO
3
in the
clinker. The alkali oxides combine preferentially with SO
3
, and if there is suﬃcient SO
3
these will crystallize to yield alkali sulfates. If there is insuﬃcient SO
3
to combine with
the alkali oxides then these may enter into solid solution in the aluminate and silicate
phases. K
2
O increases the reactivity of C
3
A while Na
2
O reduces it (Hewlett, 2004).
Therefore, the quantity of readily soluble sulfate should be adjusted to the level of
activity of C
3
A in order to optimize ﬂuidity (Hewlett, 2004). The solubility behaviour
of sulfate depends on the form in which it is present in the cement. During cement
mill (with temperatures in the range of 100 to 130
◦
C) the calcium sulfate dihydrate
(gypsum) added to the mill undergoes dehydration ﬁrst to hemihydrate and then to
soluble anhydrite. The dehydrated forms of gypsum dissolve more rapidly than gypsum
and this is beneﬁcial in ensuring that suﬃcient Ca
2+
and SO
2−
4
ions are available in
solution to control the initial reactivity of C
3
A by forming a protective layer of ettringite.
An insuﬃcient supply of soluble calcium sulfate can result in a rapid loss of workability
(ﬂash set). If a too high level of dehydrated gypsum is present, then crystals of gypsum
crystallize from solution causing false set (initial level of workability can be restored
by re-mixing). Many natural gypsums contain a proportion of the mineral natural
anhydrite, which is unaﬀected by milling temperature and dissolves slowly in the pore
solution providing SO
2−
4
ions necessary for strength optimization but having no potential
to produce false set. Thus, the optimization of readily soluble sulfate is achieved by
a combination of: control of cement total SO
3
level; controlling the level of natural
anhydrite in the calcium sulfate used and controlling cement milling temperature.
Water demand increases signiﬁcantly with the speciﬁc surface area of Portland cement
(Hewlett, 2004). For equal speciﬁc surface area, cements with narrower particle size
distribution have larger water demands due to higher volume of voids which need to be
ﬁlled with water (Hewlett, 2004). The partial replacement of clinker by ﬂy ash (as in
the case of CEM IV/B(V) 32,5N presented in Table 6.1) should result in a reduction in
water demand due to the spherical shape of particles which lubricate the mix, as can be
observed in Figure 6.3. Limestone can have a positive inﬂuence on water demand (as in
the cases of CEM II/A-L 42,5 R and CEM II/B-L 32,5 N presented in Table 6.1) because
the ﬁne limestone particles improve cement particle size distribution (see Figure 6.4),
154
6.2. Cement production quality control
Figure 6.3.: Backscattered electrons image of CEM IV/B(V) 32,5 N particles
lowering the volume of voids which must be ﬁlled with water.
Setting time
Cement paste setting behaviour is determined mainly by clinker reactivity and by cement
ﬁneness (for a given water/cement ratio). The main factors, which tend to shorten
setting time are increases in the levels of: clinker-free lime; cement ﬁneness (surface
area); C
3
S content and C
3
A content. Both ﬂy ash and slag will increase setting time
while a Portland limestone cement may have a slightly shorter setting time than the
corresponding pure Portland cement (Newman and Choo, 2003; Pera et al., 1999).
Strength development
Late strength is only slightly dependent on cement ﬁneness and for a given water/cement
ratio it is determined mainly by the chemical composition of cement (Hewlett, 2004).
While surface area is a good guide to the early rate of hydration of cement and thus early
strengths, 45 µm residue is a more reliable guide to late strengths and, in particular,
to 28 days strengths. For a given surface area, the lower the 45 µm residue the higher
the 28 days strength (Newman and Choo, 2003). The pre-hydration of cement clinker
represented by the increased loss on ignition (LOI) has a signiﬁcant inﬂuence on strength
development. However, the inﬂuence of LOI when cement contains a calcareous minor
additional constituent is much less clear (Newman and Choo, 2003). Late strengths
are normally maximized by low free lime level (as this maximizes combined silicate
content) but low free lime levels are associated with low reactivity and extended setting
times. The eﬀect of C
3
S is most pronounced at all ages whereas the contribution of
155
6. Inﬂuence of cement variations on SCC mortar/paste properties
Figure 6.4.: Backscattered electrons image of CEM II/B-L 32,5 N particles
C
2
S to strength becomes signiﬁcant only at later ages. Soluble alkalis accelerate early
strength development and depress late strengths (28 days) (Newman and Choo, 2003).
Nevertheless, the alkalis present in the crystal lattice of clinker minerals appear to
behave diﬀerently to those present in the form of soluble sulfates (Hewlett, 2004). The
total SO
3
content of the cement also inﬂuences strength properties. With increasing
SO
3
content the strength tends to increase up to an optimum value and then decreases
with even higher SO
3
contents (Hewlett, 2004). The response of a cement to a change in
cement SO
3
level is inﬂuenced by a number of factors, which include: the alkali content
and in particular the alkali sulfate (soluble alkali) content; the C
3
A level; the cement
ﬁneness. In most countries the opportunity to optimize cement SO
3
is restricted by the
upper limits for SO
3
in the relevant standards.
6.2.4. Inﬂuence of cement parameters on the properties of
mixtures including a superplasticizer (polycarboxylate type)
The presence of a superplasticizer aﬀects workability greatly (reducing the water de-
mand) but can also interfere with the nucleation and/or the growth processes, which
can inﬂuence the hydration reaction rate and the reaction products (changing setting
time and strength development). Due to the wide variability in the chemical and phys-
ical properties of cements, diﬀerent cements behave in diﬀerent ways in the presence
of the same superplasticizer. Among the cement parameters which have been found to
exert a major inﬂuence on the properties of superplasticized cement mixes (see Chapter
3), are:
• the physical characteristics of cements, such as speciﬁc surface area;
156
6.2. Cement production quality control
• the chemical characteristics of cements, such as its phase composition and the
availability of quickly soluble SO
2−
4
ions; the morphology of cement grains, espe-
cially the amount of C
3
A at their surface; the quantity of soluble alkalis;
• the quantity of clinker substituted by a mineral addition in a blended cement.
Diﬀerences in performance of various cement-admixture combinations are typically more
signiﬁcant with lower water/powder ratios (Aïtcin et al., 2001). Since PC-type super-
plasticizer is used for concrete prepared with low water/powder ratios, as in the partic-
ular case of SCC, a small diﬀerence in the dispersion action of the admixture changes
the ﬂuidity remarkably.
In Chapter 3, the mode of action of diﬀerent superplasticizers and their interaction with
cement particles was discussed. The unique molecular structure of PC type superplas-
ticizers contributes to their improved performance. The molecular structure of a PC
type superplasticizer can be designed for a particular application by changing: the main
chain length; side chain length; side chain density or type of chains (see Chapter 3,
section 3.6). Consequently, these changes can aﬀect the ability to reduce water, ﬂuidity
retention, setting time, and early strength development.
The initial reactivity of cement is critical for the performance of a PC type superplasti-
cizer. Higher dosages of superplasticizer are required with an increase of cement ﬁneness,
an increase of C
3
A phase at the surface of cement particles or an increase of quickly
soluble SO
2−
4
ions (especially, from alkali sulfate). The presence of sulfate ions in solu-
tion reduces the adsorption of PC polymers due to competitive adsorption on cement
particles. Whereas an optimum sulfate ion concentration exists for SMF and SNF, the
sulfate ion concentration should be minimized for PC type superplasticizers. Sulfates
are supplied by both alkali sulfates and calcium sulfates. The type of calcium sulfate
also matters, as dehydrated forms supply sulfate ions faster than gypsum.
As it was shown in Chapter 3, superplasticizer molecules may also adsorb on inert
powders which are added in blended cements or directly into concrete, such as ﬂy ash
and limestone ﬁller. In the particular case of limestone ﬁller, it was demonstrated
that substitution of cement clinker by limestone ﬁller is beneﬁcial in increasing the
electrostatic repulsion eﬀect of Sp.
Since many of these eﬀects may occur simultaneously, it can be diﬃcult to discover
the main factors and interactions existing between the diﬀerent components in a super-
plasticized cement suspension and this is further complicated by the ongoing hydration
reactions of cement.
157
6. Inﬂuence of cement variations on SCC mortar/paste properties
6.3. Experimental programme
In Chapter 5, SCC mortar mixes were optimized for diﬀerent Portland cement types,
namely, CEM I 52,5R, CEM I 42,5 R, CEM II/A-L 42,5 R and CEM II/B-L 32,5N,
currently used in Portugal. To evaluate the range of workability and strength variations
of SCC mortars/pastes due to diﬀerent deliveries of cement (supplied from the same
factory) four optimized mixes, corresponding to each cement type, were characterized
in the fresh, hardening and hardened states for eight cement deliveries with diﬀerent
production dates. The testing programme included tests on SCC mortars, empirical
and rheological tests on SCC pastes, experimental packing density and semi-adiabatic
tests on SCC pastes. Besides, data of standard tests on cements, physical and chemical
analysis of cements was obtained from the cement producer, for each cement delivery.
6.3.1. Materials characterization and mix proportions
The mortar mixes investigated in this study were prepared with cement, a mineral
addition (limestone ﬁller, ﬁrst delivery), CEN standardized sand and tap water. The
chemical and physical properties of the diﬀerent cement types, for diﬀerent deliveries,
are presented in Tables C.1 to C.8 of Appendix C. The physical and chemical char-
acterization of limestone ﬁller used in this study (ﬁrst delivery) is presented in Tables
C.9 and C.10 of Appendix C. A polycarboxylate type superplasticizer (V3000) was used
having a speciﬁc gravity of 1,05 and 18,5% solid content. Reference sand is siliceous
round-grain natural sand (0,08-2 mm) with a speciﬁc gravity of 2,57 and an absorption
value of 0,68%.
The mix proportions of mortar and paste, for each cement type, were established based
on the mixture parameter values presented in Table 6.2. As can be observed in Table 6.2
the mortar mixtures exhibited the same V
s
/V
m
and w/c ratios, thus Sp/p and V
w
/V
p
had to be adjusted to attain similar mortar fresh properties (Dﬂow= 260 mm and
Tfunnel=10 s), for each cement type (see Chapter 5). V
w
/V
p
values did not change
signiﬁcantly but Sp/p varied considerably with cement type.
6.3.2. Mixing sequence and testing sequence
The mortar and paste mixes were prepared in the laboratory in 1,4 and 1,25 litres
batches, respectively, and mixed in a two-speed mixer complying to NP EN 196-1. The
mixing sequence consisted of mixing sand and powder materials (or only powders, in
the case of pastes) with 0,81 of the mixing water during 60 s, stopping the mixer to
scrape material adhering to the mixing bowl, mixing for another 60 s, adding the rest of
158
6.3. Experimental programme
Table 6.2.: Optimized mixture parameters and mix proportions
CEM I 52,5R CEM I 42,5R CEM II/A-L 42,5R CEM II/B-L 32,5N
Mixture parameters
w/c 0,45 0,45 0,45 0,45
Sp/p 1,90% 1,72% 1,58% 1,49%
V
w
/V
p
0,724 0,728 0,719 0,725
V
s
/V
m
0,50 0,50 0,50 0,50
Mix proportions of mortar (kg/m
3
)
cement 466,5 468,3 464,6 466,9
limestone ﬁller 378,3 374,5 376,9 361,0
water 205,6 207,7 206,9 208,8
superpl. (V3000) 16,1 14,5 13,3 12,4
standardized sand 1286 1286 1286 1286
Mix proportions of paste (kg/m
3
)
cement 932,9 936,6 929,2 933,9
limestone ﬁller 756,6 749,0 753,9 722,0
water 393,6 397,8 396,4 400,1
superpl. (V3000) 32,1 29,0 26,6 24,7
the water with the superplasticizer, mixing for 60 s, stopping the mixer again to scrape
material adhering to the bowl, mixing for another 30 s, stopping the mixer for 5 min
and ﬁnally mixing mortar/paste during a further 30 s. The mixer was always set at low
speed except in the last 30 s of the mixing sequence where it was set at high speed.
The mortar testing sequence was approximately the following:
0 h 00 min – start of mortar mixing procedure;
0 h 10 min – start of mortar ﬂow test;
0 h 15 min – start of mortar V-funnel test;
0 h 17 min – moulding of three 70 mm cubes.
The paste testing sequence was approximately the following:
0 h 0 min – start of paste mixing procedure;
0 h 10 min – start of Marsh cone ﬂow test;
0 h 20 min – start of paste ﬂow test;
0 h 25 min – start of semi-adiabatic calorimeter test;
0 h 30 min – start of 1
st
rheological test in the rheometer;
0 h 32 min – start of centrifuge test;
159
6. Inﬂuence of cement variations on SCC mortar/paste properties
0 h 40 min – start of 2
nd
rheological test in the rheometer;
0 h 42 min – moulding of three prismatic moulds (4×4×16 mm
3
).
In general, for each cement delivery, SCC mortar and paste characterization tests were
carried out in the same day to minimize the inﬂuence of environmental factors. During
the waiting periods between tests the mortar/paste sample was covered with a humidiﬁed
cloth to avoid the loss of water by evaporation. Just before the beginning of each test
the mortar/paste sample was re-mixed by hand with a paddle (10 turns clockwise) to
destroy any structuration formed during rest (thixotropy eﬀects).
6.3.3. Mortar test methods
Mortar ﬂow and V-funnel tests
Mortar tests using the ﬂow cone and the V-funnel, with the same internal dimensions
as the Japanese equipment, were carried out to characterize fresh state (see Chapter
4, section 4.6, for details on equipments and test procedures). The mortar ﬂow test
was used to assess deformability by calculating the ﬂow diameter (Dﬂow, mortar) as
the mean of two diameters of the spread area. The V-funnel test was used to assess
the viscosity and passing ability of the mortar. Test ﬂow time was recorded (Tfunnel,
mortar).
Compressive strength at 28 days
After fresh mortar tests, three 70 mm cubes were moulded, without compaction, to
evaluate 28 day compressive strength (fc,28, mortar). Mortar cubes were demoulded one
day after casting and kept inside a chamber under controlled environmental conditions
(Temperature=20
◦
C and HR=95-98%) until testing age. Compressive strength at 28
days (fc,28, mortar) was taken as the mean of the three cubes’ compressive strength.
6.3.4. Paste test methods
Marsh cone ﬂow test
The Marsh cone ﬂow test, including the geometry of testing apparatus, is described in
NP EN 445 (Portugal. IPQ, 2002) as a method to determine the ﬂuidity of injection
grouts for rocks, soils or prestressed ducts. This test method is based on the mea-
surement of the time taken for a certain volume of material to ﬂow through the cone.
In the present study, 1000 ml of paste were poured into the cone (with an opening of
160
6.3. Experimental programme
10 mm in diameter) and the time taken for 500 ml to ﬂow out was taken as the ﬂow
time. Paste ﬂow time (Tﬂow) was given by mean of results obtained in two consecutive
measurments. Flow time varies inversely with paste ﬂuidity (Gomes, 2002; Roussel and
Le Roy, 2005).
Paste ﬂow test
The mini slump ﬂow test was carried out to assess the deformability of paste. A trun-
cated cone (with upper and lower diameters of 19 and 38 mm, respectively, and height
of 57 mm) was used in this test, which is a smaller-scale version of the Abrams cone
developed by Kantro (Gomes, 2002). After ﬁlling the cone with paste and levelling it,
the cone was lifted and the paste ﬂow diameter (Dﬂow, paste) was taken as the average
of two diameters of the spread area.
Semi-adiabatic calorimeter test
The cement hydration reactions are exothermic. In this study, the heat of reaction of
SCC pastes was measured with a simple semi-adiabatic calorimeter specially built for
this study (see Figure 6.5) based on the calorimeter described in (Juvas et al., xxxx). It
consists of a plastic box ﬁlled with insulation material forming four cavities in the centre
where four cylindrical containers can be ﬁtted. After ﬁlling these containers with cement
paste, the box was closed. The insulation material surronding cement samples avoided
rapid dissipation of heat to the environment. All tests were carried out in a cham-
ber with controlled environmental conditions (Temperature=20±2
◦
C and HR=50±5%).
The temperature evolution of cement samples was continuously monitored, during 48
hours, through the installation of temperature sensors (PT100), outside the bottom
part of each container, connected to an automatic acquisition system (datataker DT
515 Series 3) (see Figure 6.5). The mean value of three curves of temperature evolution
with time was obtained for each cement sample. From this curve three points were
distinguished (see white dots in Figure 6.6) and the following data was collected: the
initial temperature of paste; the time to start of temperature raise; the time to reach
maximum temperature and the value of maximum temperature.
Rheological tests (using a rheometer)
The viscosity and the yield stress as deﬁned by the Bingham model were evaluated using
a cone and plate rheometer (Bohlin CVO-100). The cone diameter was 40 mm with a
cone angle of 4
◦
. After placing the cement sample on the plate, the cone was lowered so
that a gap of 150 µm between the cone and plate was achieved. The temperature of the
161
6. Inﬂuence of cement variations on SCC mortar/paste properties
Figure 6.5.: Experimental set-up used in this study to measure temperature evolution
of paste under semi-adiabatic conditions
Figure 6.6.: Typical evolution of temperature of cement paste (SCC) with time obtained
from semi-adiabatic test
162
6.3. Experimental programme
(a) (b)
Figure 6.7.: (a) Typical evolution of shear stress with shear rate and (b) evolution of
viscosity with shear rate of a cement paste
samples was set at 25
◦
C. Before measurements, a pre-shear procedure was applied to
homogenize the sample, 45 s at a shear rate of 200 s
−1
, followed by a resting period of 100
s. During the measurements (control shear rate mode) the rheometer was programmed
to perform a 12-step increase of the shear rate ranging from 0,1 to 200 s
−1
and back
again to complete a full cycle. The Bingham model was adjusted to the point results
of the down-curve (see Figure 6.7). For more details on rheology principles, rheological
models and experimental procedure see Chapter 2.
Centrifuge test
A centrifuge (Centurion, model K240, series K2, see Figure 6.8) was used to compact the
solids and to determine the free water of paste, which allowed calculating the packing
density of the paste. The free water is deﬁned as the water not restricted by particles,
e.g. the water that can move around particles (Grünewald, 2004). About 30 min after
the beginning of mixing, the four plastic containers (with 50 ml volume capacity) of the
centrifuge were ﬁlled with paste (approximately 30 ml on each container). Then, cen-
trifuging was carried out for 15 min at 3500 rpm. After that, the free water which rose
up to the surface of the paste was removed with a pipette. The weight of the containers
before and after centrifuging was determined. The free water content (w
free
), in kg/m
3
,
was calculated from
w
free
=
w
final
−w
initial
30
(6.1)
163
6. Inﬂuence of cement variations on SCC mortar/paste properties
Figure 6.8.: Centrifuge (Centurion, model K240, series K2) used in the present study
where w
initial
and w
final
are the weight of containers before and after removing the sur-
plus of water, respectively. The mean value of results obtained with the four containers
was taken as the test result. From this test result the packing density of paste (PD)
can also be computed as
PD =
V
total
−V
w
V
total
−V
wfree
(6.2)
where V
total
, V
w
and V
wfree
are the volume of the sample (30 ml); the total volume of
water in the sample (obtained from the mix proportions) and the volume of free water
in the sample which can be calculated from equation 6.1.
Tensile and compressive tests at 28 days
After fresh paste tests, three prismatic moulds (4×4×16 mm
3
) were moulded, without
compaction, to evaluate 28 day tensile and compressive strengths. Paste prisms were de-
moulded one day after casting and kept inside a chamber under controlled environmental
conditions (Temperature=20
◦
C and HR=95-98%) until testing age.
6.3.5. SCC-mortar properties variation with cement delivery
The eﬀect of variations in cement delivery on SCC mortar fresh properties is presented
in Figure 6.9, along with the established acceptance limits (see also Tables E.11 to
E.14 of Appendix E). By using the quality controlled CEN sand, eﬀects on rheology
resulting from the sand were reduced to a minimum. The only parameter changing was
164
6.3. Experimental programme
(a) (b)
Figure 6.9.: Variation of (a) Dﬂow and (b) Tfunnel SCC-mortar results, with cement
delivery
cement depending on delivery. In general, a random variation of results was observed,
falling within or without exceeding considerably by far the acceptance limits, except in
the case of CEM II/B-32,5 N. Since the mix proportions were optimized for the ﬁrst
delivery and maintained constant from then on, in spite of the relatively small variation
of results, beyond the second delivery all the results fell outside the acceptance limits.
The observed variations of SCC fresh mortar properties could not be anticipated from
results of the standard water demand test, as can be observed in Figure 6.10 (a), since
this test does not take into account the eﬀect of the superplasticizer. The problem
with workability variations is that they can result in larger range of variation of the
hardened state properties, due to the occurrence of segregation or lack of ﬁlling ability.
This explains the variation of compressive strength results of SCC mortars, presented
in Figure 6.10 (b) (c.o.v. varied from 5,4 to 8,6%). Again, these results could not
be anticipated from standard compressive strength results (without superplasticizer),
which exhibited a c.o.v. varying from 2,4 to 3,8%.
6.3.6. SCC-paste properties variation with cement delivery
As mentioned before, the paste phase from the optimized mortar mixtures was also
characterized for each cement delivery, except for the ﬁrst one (see Tables E.11 to E.14
of Appendix E). The results of SCC paste ﬂow diameter and ﬂow time are presented in
Figure 6.11. These results present similar variation trend as to those of mortar ﬂow and
V-funnel tests (see Figure 6.9). In the case of pastes, the level of ﬂow time results can be
165
6. Inﬂuence of cement variations on SCC mortar/paste properties
(a) (b)
Figure 6.10.: Variation of (a) standard water demand results and (b) fc,28 SCC-mortar
results, with cement delivery
distinguished by cement type. A similar diﬀerentiation, by cement type, was observed
in the results of free water content (see Figure 6.12). Higher free water contents are
associated to lower ﬂow times in the Marsh cone. The results of packing density of
paste are related to the free water content (see section 6.3.4) but this diﬀerentiation
by cement type is not so visible in the results of packing density. The variation of
rheological parameters σ
0
and η
pl
of SCC pastes are presented in Figure 6.13. In general,
the mixtures containing CEM I 52,5 R and CEM II/B-L 32,5 N can be associated to
the highest and the lowest values of both σ
0
and η
pl
, respectively.
In Figure 6.14 the results of standard initial and ﬁnal setting times are presented for each
cement type and delivery. The results obtained with the semi-adiabatic calorimeter test
are presented in Figures 6.15 and 6.16. In general, the time needed for the temperature to
start increasing (related to initial setting time) was higher for CEM I 42,5 R followed by
CEM I 52,5 R, CEM II/A-L 42,5 R and, ﬁnally, CEM II/B-L 32,5 N. Such an eﬀect could
not be expected from the initial setting time results. This was due to the retardation
eﬀect of the superplasticizer, especially for the highest dosages. A decreasing tendency
is observed in the last four results (see Figure 6.15 (a)), for all cement types. This was
probably due to the eﬀect of the environment’s temperature. The last four tests were
carried out during summertime, thus the initial temperature of paste at the beginning
of the semi-adiabatic calorimeter test was higher (see Figure 6.16 (a)) which accelerated
the progress of initial hydration reactions. To overcome this problem the materials
(including water) should be kept in a room with controlled environmental conditions
during enough time to stabilize their temperature before mixing. Considering the time
166
6.3. Experimental programme
(a) (b)
Figure 6.11.: Variation of (a) Dﬂow and (b) Tfunnel SCC-paste results, with cement
delivery
(a) (b)
Figure 6.12.: Variation of (a) PD and (b) wfree SCC-paste results, with cement delivery
167
6. Inﬂuence of cement variations on SCC mortar/paste properties
(a) (b)
Figure 6.13.: Variation of σ
0
and η
pl
results of SCC-paste, with cement delivery
needed for SCC pastes to reach the peak temperature (related to ﬁnal setting) no large
diﬀerences were observed between cement types. The maximum temperature attained
by SCC pastes (see Figure 6.16 (b)) can be clearly diﬀerentiated by the strength class
of cement and seems to be associated with compressive strength of SCC pastes (see
Figure 6.17). The results of paste tensile strength, at 28 days, are not presented here
due to excessive variation of results within each test.
6.3.7. Correlations
A correlation analysis was performed using SPSS 15.0 commercial software. Spearman’s
correlation coeﬃcient (ρ
Spearman
) was used to discover whether there is any kind of as-
sociation between two variables (dependent and explanatory). This is a non-parametric
correlation coeﬃcient which is preferred to Pearson’s correlation coeﬃcient because it
works regardless of the distributions of the variables and is less aﬀected by outliers
(Sprent, 1993). The SCC mortar and paste test results were considered as dependent
variables and various cement characteristics as explanatory variables. Selected explana-
tory variables were: loss on ignition; surface area (Blaine); residue in the 45 µm sieve;
(Na
2
O)
equivalent
content; SO
3
content; free CaO; C
3
S, C
2
S and C
3
A contents of cement.
The results are listed in Tables 6.3, 6.4, 6.5 and 6.6 for CEM I 52,5 R, CEM I 42,5 R,
CEM II/A-L 42,5 R and CEM II/B-L 32,5 N, respectively. High absolute values of a
correlation coeﬃcient (that is values close to 1) only indicate that variables are asso-
ciated with each other, not that one variable causes another. There are also negative
relations but the important quality of these correlation coeﬃcients is not their sign but
168
6.3. Experimental programme
(a) (b)
Figure 6.14.: Variation of standard (a) initial and (b) ﬁnal setting times, with cement
delivery
(a) (b)
Figure 6.15.: Variation of (a) time to acceleration and (b) time at peak SCC-paste re-
sults, with cement delivery
169
6. Inﬂuence of cement variations on SCC mortar/paste properties
(a) (b)
Figure 6.16.: Variation of (a) initial temperature and (b) temperature at peak SCC-paste
results, with cement delivery
Figure 6.17.: Variation of fc,28 SCC-paste results, with cement delivery
170
6.3. Experimental programme
Table 6.3.: Correlation matrix within cement characteristics and between cement char-
acteristics and SCC mortar/paste test results, corresponding to CEM I 52,5
R
SA resid. free
LOI (Blaine) 45µm (Na
2
O)
eq.
SO
3
CaO C
3
S C
2
S C
3
A
LOI 1,000
SA (Blaine) -,571 1,000
resid. 45µm ,084 ,180 1,000
(Na
2
O)
eq.
-,214 ,357 ,060 1,000
SO
3
-,690 ,524 -,347 ,357 1,000
free CaO -,301 ,590 ,758 ,048 ,133 1,000
C
3
S -,321 ,143 ,018 -,107 -,429 -,164 1,000
C
2
S ,214 -,214 ,072 ,000 ,286 ,109 -,714 1,000
C
3
A -,143 ,071 ,000 ,143 ,179 ,436 -,357 -,179 1,000
Dﬂow
a
-,310 ,132 ,220 -,229 -,667 ,000 ,577 ,071 -,649
Tfunnel
a
,167 -,084 -,268 ,169 ,786 -,205 -,505 -,143 ,685
fc,28
a
,357 ,096 -,659 -,060 ,095 -,024 -,468 ,071 -,342
Dﬂow
b
-,036 ,345 ,449 -,618 -,054 -,523 ,355 -,252 -,164
Tﬂow
b
,107 ,288 -,593 -,360 ,214 ,109 -,757 ,393 -,054
fc,28
b
-,321 ,703 -,445 -,108 -,643 ,273 -,054 ,750 -,667
PD
b
-,321 ,018 -,222 ,126 -,929 ,109 ,523 ,393 -,775
w
free
b
-,321 ,018 -,222 ,126 -,929 ,109 ,523 ,393 -,775
σ
0
b
,071 -,595 -,185 ,649 ,000 ,327 ,036 ,036 ,252
η
pl
b
,000 -,450 ,000 ,054 ,429 -,218 -,108 -,071 ,487
Temp. initial
b
-,107 -,847 ,704 ,126 ,357 -,600 ,595 -,714 ,505
Time acceler.
b
-,036 ,345 ,449 -,618 -,054 -,523 ,355 -,252 -,164
Time peak
b
,107 ,288 -,593 -,360 ,214 ,109 -,757 ,393 -,054
Temp. peak
b
-,321 ,703 -,445 -,108 -,643 ,273 -,054 ,750 -,667
Note: correlation values typed bold and underlined are signiﬁcant at the 0,01 level (2-tailed) and
correlation values only typed bold are signiﬁcant at the 0,05 level (2-tailed);
a
mortar test results;
b
paste test results
their absolute value.
High correlations between explanatory variables increase the diﬃculty of associating
certain mortar/paste properties to cement properties because it is not clear how much
of an eﬀect should be attributed to each mortar/paste property. In this study, signif-
icant correlations were found between (free CaO) and (residue 45 µm) for all cement
types, except CEM I 42,5 R. In the case of CEM II/A-L 42,5 R mixtures, a signiﬁcant
correlation was also found between (LOI) and (residue 45 µm). A signiﬁcant correlation
was found between (C
3
A) and (C
2
S) for the case of CEM I 42,5 R.
Considering CEM II/B-L 32,5 N mixture results, which exhibited the largest devia-
tion from target, the SO
3
content of cement was the most relevant eﬀect to explain
the observed variations in mortar Tfunnel, paste Tﬂow, paste PD and w
free
and max-
171
6. Inﬂuence of cement variations on SCC mortar/paste properties
Table 6.4.: Correlation matrix within cement characteristics and between cement char-
acteristics and SCC mortar/paste test results, corresponding to CEM I 42,5
R
SA resid. free
LOI (Blaine) 45µm (Na
2
O)
eq.
SO
3
CaO C
3
S C
2
S C
3
A
LOI 1,000
SA (Blaine) -,071 1,000
resid. 45µm ,228 ,144 1,000
(Na
2
O)
eq.
-,071 ,190 -,192 1,000
SO
3
,571 -,167 -,132 ,476 1,000
free CaO ,310 ,143 ,144 -,452 ,071 1,000
C
3
S -,414 -,288 -,054 ,414 -,234 -,450 1,000
C
2
S ,296 ,667 ,037 -,630 -,482 ,334 -,224 1,000
C
3
A ,036 -,429 ,286 ,571 ,464 -,500 ,000 -,815 1,000
Dﬂow
a
,000 ,167 ,275 -,595 -,690 ,143 -,559 ,556 -,179
Tfunnel
a
-,024 ,190 -,204 ,762 ,476 -,548 ,631 -,296 ,250
fc,28
a
-,214 ,310 -,443 ,310 ,190 ,286 ,450 ,222 -,714
Dﬂow
b
,179 ,571 ,714 -,214 -,643 ,071 -,054 ,408 ,000
Tﬂow
b
,500 ,000 -,429 ,393 ,571 -,500 -,288 ,037 ,250
fc,28
b
,000 ,000 -,464 -,357 -,071 ,179 ,288 ,556 -,821
PD
b
-,036 -,321 ,143 -,464 ,214 ,750 -,468 -,259 ,000
w
free
b
-,036 -,321 ,143 -,464 ,214 ,750 -,468 -,259 ,000
σ
0
b
,357 -,536 -,750 ,393 ,821 -,143 -,090 -,259 ,107
η
pl
b
,357 -,536 -,750 ,393 ,821 -,143 -,090 -,259 ,107
Temp. initial
b
-,107 -,321 ,357 ,357 ,071 ,107 -,018 -,704 ,679
Time acceler.
b
-,357 -,107 -,179 -,250 -,393 ,286 ,631 ,259 -,679
Time peak
b
-,179 ,000 -,321 ,107 -,357 -,107 ,775 ,334 -,571
Temp. peak
b
,143 -,464 ,179 -,143 ,571 ,357 -,577 -,556 ,500
Note: correlation values typed bold and underlined are signiﬁcant at the 0,01 level (2-tailed) and
correlation values only typed bold are signiﬁcant at the 0,05 level (2-tailed);
a
mortar test results;
b
paste test results
172
6.3. Experimental programme
Table 6.5.: Correlation matrix within cement characteristics and between cement char-
acteristics and SCC mortar/paste test results, corresponding to CEM II/A-L
42,5 R
SA resid. free
LOI (Blaine) 45µm (Na
2
O)
eq.
SO
3
CaO C
3
S C
2
S C
3
A
LOI 1,000
SA (Blaine) ,635 1,000
resid. 45µm -,790 -,667 1,000
(Na
2
O)
eq.
-,228 ,071 -,238 1,000
SO
3
,467 ,500 -,143 -,619 1,000
free CaO -,518 -,467 ,790 -,395 ,204 1,000
C
3
S -,162 -,071 -,214 ,714 -,571 -,739 1,000
C
2
S ,703 ,321 -,536 -,071 ,214 ,036 -,536 1,000
C
3
A -,200 -,018 ,396 -,613 -,108 ,145 -,198 -,270 1,000
Dﬂow
a
-,407 ,190 ,214 ,071 ,310 ,084 ,250 -,643 -,054
Tfunnel
a
,838 ,595 -,667 -,024 ,071 -,635 ,107 ,536 ,108
fc,28
a
,395 ,643 -,357 ,119 ,214 -,419 ,107 ,107 ,180
Dﬂow
b
-,505 ,179 ,429 ,107 ,179 ,252 ,286 -,607 ,108
Tﬂow
b
,721 ,750 -,893 ,393 ,107 -,468 -,036 ,714 -,216
fc,28
b
-,306 ,393 -,036 ,143 -,143 -,180 ,250 -,429 ,595
PD
b
-,667 -,643 ,929 -,393 -,179 ,487 ,107 -,607 ,306
w
free
b
-,667 -,643 ,929 -,393 -,179 ,487 ,107 -,607 ,306
σ
0
b
,342 -,143 ,000 -,643 ,214 ,234 -,714 ,464 ,270
η
pl
b
,090 ,107 ,286 -,786 ,643 ,631 -,893 ,214 ,378
Temp. initial
b
,054 -,214 ,321 -,679 ,321 ,775 -,929 ,571 ,126
Time acceler.
b
-,396 ,036 ,393 ,179 ,036 -,072 ,607 -,786 ,036
Time peak
b
-,144 ,071 ,536 -,250 ,536 ,306 ,107 -,500 -,072
Temp. peak
b
-,180 -,214 -,179 -,036 -,393 ,018 -,321 ,214 ,324
Note: correlation values typed bold and underlined are signiﬁcant at the 0,01 level (2-tailed) and
correlation values only typed bold are signiﬁcant at the 0,05 level (2-tailed);
a
mortar test results;
b
paste test results
173
6. Inﬂuence of cement variations on SCC mortar/paste properties
Table 6.6.: Correlation matrix within cement characteristics and between cement char-
acteristics and SCC mortar/paste test results, corresponding to CEM II/B-L
32,5 N
SA resid. free
LOI (Blaine) 45µm (Na
2
O)
eq.
SO
3
CaO C
3
S C
2
S C
3
A
LOI 1,000
SA (Blaine) -,571 1,000
resid. 45µm ,084 ,180 1,000
(Na
2
O)
eq.
-,214 ,357 ,060 1,000
SO
3
-,690 ,524 -,347 ,357 1,000
free CaO -,301 ,590 ,758 ,048 ,133 1,000
C
3
S -,321 ,143 ,018 -,107 -,429 -,164 1,000
C
2
S ,214 -,214 ,072 ,000 ,286 ,109 -,714 1,000
C
3
A -,143 ,071 ,000 ,143 ,179 ,436 -,357 -,179 1,000
Dﬂow
a
,357 -,881 -,287 -,405 -,524 -,590 ,179 -,321 ,179
Tfunnel
a
-,619 ,524 -,252 ,738 ,857 ,012 -,250 ,286 -,036
fc,28
a
-,071 -,190 ,611 ,405 -,333 ,157 ,357 -,250 -,071
Dﬂow
b
,214 -,714 ,180 ,071 -,143 ,000 -,393 ,036 ,643
Tﬂow
b
-,643 ,429 -,649 ,071 ,893 ,109 -,429 ,107 ,571
fc,28
b
,286 -,500 -,216 ,571 -,286 -,764 ,286 -,429 -,107
PD
b
,643 -,429 ,649 -,071 -,893 -,109 ,429 -,107 -,571
w
free
b
,643 -,429 ,649 -,071 -,893 -,109 ,429 -,107 -,571
σ
0
b
,393 ,536 ,577 -,036 -,536 ,600 ,250 -,393 ,107
η
pl
b
-,571 ,750 -,162 -,214 ,500 ,655 -,143 -,036 ,536
Temp. initial
b
-,107 ,893 -,072 -,107 ,179 ,273 ,179 -,107 -,321
Time acceler.
b
,536 -,536 ,126 ,500 -,286 -,218 -,286 -,179 ,500
Time peak
b
,214 -,714 ,180 ,071 -,143 ,000 -,393 ,036 ,643
Temp. peak
b
-,643 ,429 -,649 ,071 ,893 ,109 -,429 ,107 ,571
Note: correlation values typed bold and underlined are signiﬁcant at the 0,01 level (2-tailed) and
correlation values only typed bold are signiﬁcant at the 0,05 level (2-tailed);
a
mortar test results;
b
paste test results
174
6.3. Experimental programme
(a) (b)
Figure 6.18.: Eﬀects of (a) SO
3
and (b) (Na
2
O)
equivalent
on Tfunnel of mortars incorpo-
rating CEM II/B-L 32,5 N
imum temperature of paste (see Table 6.6). A signiﬁcant correlation was also found
between mortar Tfunnel and (Na
2
O)
equivalent
content of cement. The eﬀect of surface
area (Blaine) was signiﬁcant to explain the variations of mortar Dﬂow and the temper-
ature of paste in the beginning of the semi-adiabatic calorimeter test (see Table 6.6).
Based on the experience of the cement producer, the speciﬁc surface area obtained by
the Blaine method is not a good indicator of Type II cement ﬁneness. In this case, the
50% percentile (D0,5) of the particle size distribution is a better indicator of cement
ﬁneness, but no signiﬁcant correlation was found between (D0,5) and (Dﬂow, mortar)
nor between (D0,5) and (Temp initial), for CEM II/B-L 32,5 N results (ρ
Spearman
=0,048,
ρ
Spearman
=-0,286, respectively). Thus, only the eﬀects of SO
3
and (Na
2
O)
equivalent
con-
tents were considered the most relevant for the observed variations, in the fresh state
(see Figure 6.18). It can be observed that mortar Tfunnel increased with an increase of
SO
3
and (Na
2
O)
equivalent
contents in cement.
By analysing the correlation matrixes corresponding to CEM I 52,5 R and CEM I 42,5
R, the eﬀects of SO
3
and/or (Na
2
O)
equivalent
always appeared strongly associated to
more than one fresh state SCC mortar/paste property (see Tables 6.3 and 6.4). In the
case of CEM II/A-L 42,5 R, the eﬀect of (residue 45 µm) seems to be the most signiﬁcant
to explain fresh state variations (see Table 6.5). Note that the LOI eﬀect (associated
to mortar Tfunnel) is also associated to (residue 45 µm) (see Table 6.5). As can be
observed in Figure 6.19, an increase of residue in 45 µm sieve (an increase of coarser
cement particles) increases ﬂuidity of SCC mortar/paste.
Based on the literature review, the inﬂuences of SO
3
and/or (Na
2
O)
equivalent
and of
175
6. Inﬂuence of cement variations on SCC mortar/paste properties
(a) (b)
Figure 6.19.: Eﬀect of residue 45 µm on (a) mortar Tfunnel and (b) paste Tﬂow of
mixtures incorporating CEM II/A-L 42,5 R
the residue in 45 µm sieve could be expected (see section 6.2.4). An increase of SO
3
and/or (Na
2
O)
equivalent
contents of cement can be associated to an increase of readily
soluble sulfates in cement which reduce the dispersing action of PC type superplasti-
cizer, lowering the amount of free water and, consequently, reducing ﬂuidity (indicated
by higher ﬂow time results of mortar and paste). On the other hand, a reduction of
residue in 45 µm sieve can be associated to a larger surface area of particles, thus a
larger consumption of superplasticizer for the same dosage and, consequently, a lower
dispersion eﬀect and a reduction of ﬂuidity.
6.4. SCC paste rheology
Rheology of cement paste largely dictates concrete rheology, given a speciﬁed aggregates
skeleton (Ferraris et al., 2001b; Martys and Ferraris, 2002). The key role of the paste
is clearly shown by the strong eﬀect on concrete workability of the powder materials,
water/powder ratio and of superplasticizer dosage. Furthermore, important phenomena
in fresh concrete, like air entrapment and aggregates segregation are determined from
paste rheology (along with the stabilising eﬀect of smaller aggregate particles), which
provides more or less freedom of motion to aggregates and air bubbles. In spite of
importance of paste rheology, there is no generally accepted procedure for its study,
and so direct comparison between diﬀerent works and the deﬁnition of rheology-based
acceptance criteria for SCC paste is often diﬃcult.
176
6.4. SCC paste rheology
Ultimately, it would be desirable to predict concrete behaviour from the paste charac-
teristics. Tests on paste are easier to carry out and require less material. Although
rheological tests require more expensive equipment and speciﬁc training, they allow a
more fundamental and precise characterization of cement suspensions (see Chapter 2).
Since there is some consensus concerning target mortar properties for SCC (EFNARC,
2002; Okamura et al., 2000), in this study, mortar mixtures and corresponding paste
mixtures were characterized in parallel, so that target SCC paste properties could be
found. Furthermore, pastes were characterized by means of empirical and rheological
(using a rheometer) tests. Thus, a relation between empirical test results and rheolog-
ical tests could be deﬁned, on the paste level. These relations may be of interest for
industrial labs to transform empirical data, in arbitrary units, into relevant rheologi-
cal parameters. Rheological parameters are intrinsic properties of the material while
empirical test results are test geometry and material density dependant.
6.4.1. Experimental programme
The present experimental programme is an extension of the experimental programme
described in section 6.3; optimized mortar mixtures with sand contents diﬀerent from
0,5 and the corresponding paste mixtures were included in the study, in order to obtain
a larger range of paste properties. The materials used were the same, and all cement
deliveries were tested. The mix proportions of additional mortar and paste mixtures for
each cement type were established based on the mixture parameter values presented in
Table 6.7 and the respective paste test results are summarized in Tables E.11 to E.14
of Appendix E. Mixing and testing sequences were maintained (see section 6.3.2).
6.4.2. Rheology vs. empirical test results
In Figure 6.20 ﬂow time results of pastes in the Marsh cone are plotted against the
measured viscosity at 50 s
−1
of shear rate, from the down-curve, measured at 35 minutes
after the beginning of mixing. The corresponding Spearman’s correlation coeﬃcient was
0,902 (signiﬁcant at the 0,01 level (two-tailed)) indicating that results of both tests are
clearly related. However one notices that the scatter of both ﬂow time and viscosity
increased at increasing ﬂow time. This was also found by other authors (Gomes, 2002;
Grünewald and Walraven, 2005; Le Roy and Roussel, 2005). The correlation coeﬃcient
between ﬂow time and viscosity decreased for lower shear rates. For example, the
correlation coeﬃcient between ﬂow time and the viscosities measured at 12,6 s
−1
and
1,6 s
−1
was 0,858 and 0,853, respectively. At higher shear rates, the particles are ‘fully’
dispersed thus the results are less aﬀected by time eﬀects and shear history, which
177
6. Inﬂuence of cement variations on SCC mortar/paste properties
Table 6.7.: Optimized mixture parameters and mix-proportions
CEM I 52,5R CEM I 42,5R CEM II/A-L 42,5R CEM II/B-L 32,5N
Mixture parameters
w/c 0,43 0,41 0,38 0,36 0,37 0,33 0,4 0,35
Sp/p 1,99% 2,05% 1,86% 1,89% 1,83% 1,89% 1,49% 1,41%
V
w
/V
p
0,716 0,700 0,710 0,704 0,713 0,705 0,714 0,714
V
s
/V
m
0,49 0,47 0,45 0,43 0,45 0,40 0,45 0,4
Mix proportions of mortar (kg/m
3
)
cement 494,8 532,5 601,1 654,3 618,5 751,9 572,7 714,1
limestone ﬁller 373,0 379,3 346,4 335,0 323,1 288,8 349,3 300,3
water 207,3 211,3 221,9 227,8 222,7 239,1 225,8 245,3
superpl. (V3000) 17,2 18,7 17,6 18,7 17,2 19,6 13,8 14,3
standardized sand 1260 1209 1157 1106 1157 1029 1157 1029
Mix proportions of paste (kg/m
3
)
cement 970,1 1004,6 1092,9 1147,9 1124,6 1253,2 1041,3 1190,2
limestone ﬁller 731,4 715,6 629,9 587,6 587,4 481,3 635,0 500,5
water 389,6 383,2 389,2 386,5 390,6 386,9 396,1 397,1
superpl. (V3000) 33,8 35,2 32,0 32,8 31,3 32,7 25,0 23,8
might explain the lower dispersion of results and the higher correlation coeﬃcients.
A similar relation was also found between ﬂow time and plastic viscosity (Bingham
model) (ρ
Spearman
=0,894). The viscosity is almost equal to the plastic viscosity at high
shear rates. However, at lower shear rates the viscosity is much higher than the plastic
viscosity.
According to Roussel and Le Roy (2005) ﬂow time is proportional to viscosity, but yield
stress has to be taken into account to predict ﬂow time of non-Newtonian ﬂuids, like
the case of cement pastes. The presence of yield stress increases the time needed for
a certain amount to ﬂow out of the cone (Roussel and Le Roy, 2005). The following
relation between ﬂow time and rheological parameters of paste was derived by (Roussel
and Le Roy, 2005) and is given by
Tflow =
a
v
µ
pl
ρ −b
v
σ
0
(6.3)
where a
v
and b
v
are constants, depending on cone geometry and the observed ﬂowing
volume V, ρ is the material density, and η
pl
and σ
0
are the plastic viscosity and yield
stress, respectively. The constants a
v
and b
v
can either be calibrated using known ma-
terials or calculated using the following expressions
a
v
=
8V tan(α)

[3πr
3
tan(α)gr(h + H
0
)] (H
0
tan(α) + r)
3
(6.4)
178
6.4. SCC paste rheology
Figure 6.20.: Relation between the viscosity at a shear rate of 50 s
−1
and ﬂow time of
pastes (ρ
Spearman
=0,902)
b
v
=
πr
3
[8r ln(H
0
tan(α) + r) −8r ln(r) + 8htan(α)]
3πr
3
tan(α)gr(h + H
0
)
(6.5)
where α, r, H
0
and h can be obtained from cone geometry and ﬁlling volume V , as
indicated in Figure 6.21, and g is the gravitational constant (Roussel and Le Roy, 2005).
a
v
and b
v
are very sensitive to r and h, which sometimes are diﬃcult to measure, therefore
estimation of these constants is sometimes preferred (Roussel and Le Roy, 2005). A
comparison between measured ﬂow time and predicted ﬂow time using equation 6.3 is
given in Figure 6.22, with a
v
=154503 s
2
/m
2
and b
v
=25,5 s
2
/m
2
. From this ﬁgure
it can be observed that up to a ﬂow time approximately 50 s the model given by
equation 6.3 adequately predicts the measured ﬂow times. For ﬂow times above 50
s data points start to deviate from the y = x line. This may be due to diﬀerences in
how yield stress is determined.
Several authors reported a relation between yield stress and slump, as well as, spread
ﬂow (of concrete, mortars or paste) (Flatt et al., 2006; Roussel and Coussot, 2005; Saak
et al., 2004; Roussel et al., 2005). However, diﬀerent relations can be found in the lit-
erature. A comparison between two models relating yield stress with spread radius and
its range of validity is presented in (Flatt et al., 2006). Flatt et al. (2006) clariﬁed that
Saak’s model (ﬁrst developed by Murata) (Saak et al., 2004) and given by
σ
0
=
ρV g
√
3πR
2
(6.6)
where ρ and V is the density and volume of the sample (assuming that the cone is
179
6. Inﬂuence of cement variations on SCC mortar/paste properties
Figure 6.21.: α, r, H
0
and h dependant on cone geometry and ﬁlling volume (Roussel
and Le Roy, 2005)
Figure 6.22.: Comparison between measured and predicted ﬂow time
180
6.4. SCC paste rheology
Figure 6.23.: Relation between the yield stress and ﬂow diameter of pastes (ρ
Spearman
=
0,902)
completely ﬁlled up), respectively, and R is the radius of the spread, performes well at
low spread radius and not at large spread radius. In contrast, Roussel’s model given by
σ
0
= 1, 747ρV
2
R
−5
−λ
R
2
V
(6.7)
where ρ and V is the density and volume of the sample, respectively; R is the radius of
the spread and λ is a constant (Roussel et al., 2005), was found to be more reliable for
large spreads (Flatt et al., 2006). λ is a function of both unknown tested ﬂuid surface
tension and contact angle. A value of 0,005 was adopted by Roussel et al. (2005). In
addition, an exponential function of the type of
σ
0
= a exp(−bR) (6.8)
where a and b are ﬁtting parameters, was found to approximately ﬁt experimental data
over a wide range of diameters of interest (Flatt et al., 2006). The data collected in
the present work supports this conclusion, as illustrated in Figure 6.23. In this ﬁgure
mini-slump ﬂow diameter results of pastes are plotted against the yield stress (Bingham
model), for measurements carried out at 35 minutes after begining of the mixing. The
corresponding Spearman’s correlation coeﬃcient was 0,902 (signiﬁcant at the 0,01 level
(two-tailed)).
The predictions of both Saak and Roussel models (with λ equal to 0 and 0,005) models
according to equations 6.6 and 6.7 are given in Figure 6.24, for the range of spread diam-
eters that can be obtained with Kantro’s mini-slump cone. As can be observed in this
181
6. Inﬂuence of cement variations on SCC mortar/paste properties
(a) (b)
Figure 6.24.: Relation between yield stress and spread diameter calculated with Saak’s
model and Roussel’s model (with both λ=0 and 0,005) for the mini-slump
cone (Kantro) and experimental data collected in the present study
ﬁgure the yield stress data collected in the present work is closer to the curve described
by Roussel’s model with surface tension eﬀects included (λ=0,005). Nevertheless, this
model failed to explain the relation between the spread diameter and yield stress results
obtained in the current study. The divergence of data from Roussel’s model can also be
attributed to diﬀerences in how yield stress is determined.
6.4.3. Comparison of paste and mortar characteristics
The complete experimental programme included mortar mixtures with V
s
/V
m
ranging
from 40 to 50%, w/c ratio ranging from 0,33 to 0,45, Sp/p ranging from 1,4% to 2%, and
four diﬀerent cement types. Mortar mix proportions were adjusted to attain similar fresh
properties (Dﬂow= 260 mm and Tfunnel=10 s), for each cement type (see Table 6.2 and
Table 6.7). In addition, changes in mortar/paste properties were introduced due to
variations in cement, from diﬀerent deliveries. Altogether, this resulted in a large range
of paste properties, which correspond to ‘good’ or ‘near-good’ SCC mortars, as can
be observed in Figure 6.25. The paste properties which led to mortar mixtures with
Dﬂow=260±10 mm and Tfunnel=10±2 s, considered here as ‘good’ SCC mortars, are
identiﬁed with a red dot in Figure 6.25. These are spread over the entire area except in
the lower part on the right, for Dﬂow values higher than about 170 mm. Thus, one can
conclude that there is a target area of paste properties which can lead to ‘good’ SCC
mortars (exhibiting Dﬂow=260±10 mm and Tfunnel=10±2 s, simultaneously).
182
6.4. SCC paste rheology
Figure 6.25.: Range of properties of the analysed pastes incorporating diﬀerent cement
types
In Figure 6.26 data points are distinguished in terms of ﬁne aggregate content (V
s
/V
m
)
instead of cement type. From this ﬁgure it can be clearly seen that target paste prop-
erties are related with aggregate content. Mortars with higher ﬁne aggregate content
(V
s
/V
m
) require more ﬂuid pastes to be ﬂowable, while mortars with lower ﬁne ag-
gregate content require more viscous pastes to be stable. This is in agreement with
the estimated responses of pastes corresponding to optimized mortar mixtures, with
varying aggregate content, shown in Figure 6.27. These mortar mixtures incorporated
CEM I 52,5 R, limestone ﬁller, superplasticizer V3005 and reference sand (see Chapter
5, paragraph 5.10.6). A central composite design was also carried out to mathematically
model the inﬂuence of the three mixture parameters (w/c, V
w
/V
p
and Sp/p) and their
coupled eﬀects on Dﬂow and Tﬂow responses of paste mixtures.
Based on the target area deﬁned in Figure 6.26 and on the mini-slump ﬂow and Marsh
ﬂow tests the mixtures can be optimized ﬁrst on the paste level to minimize the num-
ber of tests on the mortar/concrete levels. However, the variability of the ﬂow time
results and loss of ﬂuidity with time increases with increasing paste viscosity, which is
a major disadvantage of the Marsh ﬂow test. This was also found by Gomes (2002) and
Le Roy and Roussel (2005). In this study, a strong relation was found between ﬂow time
(Marsh cone) and free water (centrifuge test) results (see Figure 6.28) (ρ
Spearman
=0,986;
signiﬁcant at the 0,01 level (two-tailed)). Therefore, the centrifuge test is a promis-
ing substitute test of the Marsh cone with the advantage of being equally precise for
lower and higher ﬂuidity pastes. The target area for the (Dﬂow, w
free
) paste results is
presented in Figure 6.29.
183
6. Inﬂuence of cement variations on SCC mortar/paste properties
Figure 6.26.: Range of properties of the analysed pastes, corresponding to diﬀerent
V
s
/V
m
Figure 6.27.: Estimated responses of pastes corresponding to optimized mortar mixtures
(CEM I 52,5 R+limestone ﬁller+V3005)
184
6.4. SCC paste rheology
Figure 6.28.: Relation between wfree and Tﬂow of SCC pastes (ρ
Spearman
=0,986)
Figure 6.29.: Range of properties of the analysed pastes
185
6. Inﬂuence of cement variations on SCC mortar/paste properties
Figure 6.30.: Range of rheological parameters of the analysed pastes
In Figure 6.30 the range of rheological parameters values (Bingham model) obtained in
this study are presented along with the identiﬁcation of those which led to ‘good’ SCC
mortars. ’Good’ SCC mortars were found for pastes with a plastic viscosity ranging from
about 0,3 to 0,8 Pa.s and a yield stress ranging from 0,5 to 2,5 Pa. It should be mentioned
that the target areas deﬁned in Figures 6.26, 6.29 and 6.30 will probably change if a
diﬀerent superplasticizer or a viscosity agent (admixture or a very ﬁne material) is used
(Grünewald and Walraven, 2005; Saak, 2000).
6.4.4. Comparison of paste and concrete characteristics
In order to understand the inﬂuence of paste characteristics in the ﬁnal SCC mix-
proportions, two diﬀerent pastes, incorporating CEM II/A-L 42,5 R, were selected from
Figure 6.26 for the studies carried out at the concrete level. The paste mix-proportions
are given in Table 6.8 along with the respective test results. These mix-proportions
were maintained at the concrete level and only the mixture parameters related with the
aggregate skeleton were changed to adjust the mixtures, namely, V
s
/V
m
, V
g
/V
g,lim
and
s
1
/s. A central composite design was carried out to mathematically model the inﬂuence
of the three mixture parameters (V
s
/V
m
, V
g
/V
g,lim
and s
1
/s) and their coupled eﬀects
on Dﬂow, Tfunnel and H2/H1 responses of concrete mixtures. In Figures 6.31 and 6.32
the mix-proportions range to obtain an SCC of classes SF2, VF2 and PL2 are given for
mixtures incorporating Paste A and B, respectively, while maintaining s
1
/s=0,50. From
these ﬁgures it can be clearly seen that the most ﬂuid paste (A) led to mixtures with
higher content of ﬁne aggregate and lower coarse aggregate content (higher total surface
area of aggregates); while the less ﬂuid paste allowed incorporating higher content of
186
6.4. SCC paste rheology
Table 6.8.: Pastes selected for the study at the concrete level
Paste A Paste B
Mix-proportions w/c 0,45 0,37
Sp/p 1,58% 1,83%
V
w
/V
p
0,719 0,713
Paste test results Dﬂow (mm) 165,5 151,0
Tﬂow (s) 24,4 40,0
wfree (kg/m
3
) 79,5 71,1
η
pl
(Pa.s) 0,28 0,45
σ
0
(Pa) 0,52 0,70
Figure 6.31.: Range of mix-proportions to obtain an SCC (of classes SF2, VF2 and PL2),
incorporating Paste A and maintaining s
1
/s=0,50
coarse aggregate together with lower ﬁne aggregate content (lower total surface area of
aggregates). It should be mentioned that in this study very favourable aggregates were
used, namely, two natural rounded sands and limestone coarse aggregate (maximum size
of 16 mm). The target areas presented in Figures 6.31 and 6.32 will probably change
by changing aggregates type.
These conclusions are in agreement with the concept of “Concrete Equivalent Mortar”
(CEM) introduced by Schwartzentruber and Catherine (2000). In this method, CEM
mix proportions are calculated from the corresponding concrete mixture maintaining
sand (particles smaller than 5 mm), cement, addition and superplasticizer types; water
to cement ratio; superplasticizer to cement ratio; addition to cement ratio and surface
of aggregates to cement ratio (using extra sand to replace particles of coarse aggregate
larger than 5 mm) (Schwartzentruber and Catherine, 2000). A correlation was found
187
6. Inﬂuence of cement variations on SCC mortar/paste properties
Figure 6.32.: Range of mix-proportions to obtain an SCC (of classes SF2, VF2 and PL2),
incorporating Paste B and maintaining s
1
/s=0,50
between workability of CEM and corresponding concrete, depending on aggregates used
(Schwartzentruber and Catherine, 2000; Nachbaur et al., 2005). Therefore, ﬁne aggre-
gate content of CEM increases with surface area of aggregates in the concrete mixture.
To obtain concrete from mortars studied in Chapter 5, instead of replacing part of the
ﬁne aggregate by coarse aggregate (as is the case of the CEM method) all ﬁne aggregate
(standard sand) must be replaced by current aggregates. In spite of that diﬀerence,
one can conclude that optimized mortars with higher sand content correspond to con-
crete mixtures with higher total surface area of aggregates, i.e. richer in ﬁne aggregate
particles, and lower paste contents.
To sum up, content and distribution of aggregates (aﬀecting the speciﬁc surface area)
and paste characteristics must be known to predict behaviour of SCC mortars/concrete
in the fresh state.
6.5. Final remarks
In the present study, diﬀerent cement samples from the same production line were anal-
ysed. The only parameter changing was production date. It was shown that large devia-
tions from target workability properties can occur depending only on cement delivery, as
occurred for CEM II/B-L 32,5 N. Thus, cement variations and cement-superplasticizer
interaction should be taken into account when discussing robustness of SCC mixtures.
It is important to mention that the observed variations could have been diﬀerent in the
presence of another superplasticizer type.
188
6.5. Final remarks
This study highlighted the importance of an eﬃcient quality control system to detect
large deviations from target and hence implement corrective actions in order to main-
tain results in conformity with performance requirements. However, standard tests
on cement like setting time and water demand are not adequate because they do not
incorporate admixtures and do not give any indication about cement-superplasticizer
interaction. It was demonstrated that analysing cement mortars with equivalent com-
position to the concrete mixture is eﬀective for detecting workability variations and
cement-superplasticizer compatibility problems. Eﬀects of aggregate on test results are
minimised by using standardised sand. Besides, mortar tests are simple, easy to carry
out and require smaller amounts of material than concrete tests.
Based on obtained results, the mortar and paste ﬂow tests and the centrifuge test are the
most sensitive tests to detect the causes behind workability variations of SCC mixtures.
The problem with cone ﬂow tests is that they are not suitable for assessing pastes with
low ﬂuidity. Thus, the centrifuge test is a promising substitute test of the Marsh cone
test with the advantage of being equally precise for lower and higher ﬂuidity pastes.
Many factors inﬂuence ﬂuidity and the hydration process of cement paste. Some of
these factors may also have synergistic eﬀects. This makes it diﬃcult to point out
one particular parameter, which is responsible for producing a certain property. A
more ‘robust’ measure of association between two variables, the Spearman’s correlation
coeﬃcient, was recommended here instead of a classical correlation coeﬃcient. Moreover,
the quality information presently available from the cement supplier is not detailed
enough. Physical and chemical parameters from cement characterization are necessary
for explaining the variation of early age properties of SCC mixtures. In this study, the
SO
3
and/or (Na
2
O)
equivalent
contents of cement and the residue in the 45 µm sieve were
found to be associated to ﬂuidity changes of SCC mortar/paste mixtures.
As expected, it was found that empirical test results correlate with the rheological
parameters of pastes, namely, yield stress and plastic viscosity. Furthermore, the existing
models for predicting the mini-slump ﬂow diameter and the ﬂow time of pastes were
implemented and compared with experimental data. A reasonable approximation was
found between measured and predicted values, but a better approximation could be
expected if the same protocol to determine yield stress (mixing sequence, test equipment,
tool geometry, testing sequence) had been followed.
The target paste properties, for diﬀerent ﬁne aggregate content, were deﬁned in terms
of both empirical test results and rheological parameters, linking mortar and paste
properties that are adequate for SCC. The inﬂuence of paste characteristics on the ﬁnal
SCC mix proportions was discussed based on two experimental studies, carried out at
concrete level, and on the “Concrete Equivalent Mortar” approach. It was concluded
that optimized mortar mixtures with higher sand content (see Chapter 5) will lead to
189
6. Inﬂuence of cement variations on SCC mortar/paste properties
SCC mixtures with higher total surface area of aggregates, i.e. richer in ﬁne aggregate
particles, and lower paste contents.
190
7. Evaluation of SCC mixtures
robustness
7.1. Introduction
This chapter presents a methodology to assess robustness of SCC mixtures. This
methodology (LABEST/FEUP methodology) was developed in three phases: ﬁrst, the
experimental phase conducted according to a central composite design; second, the sta-
tistical analysis and model ﬁtting of data collected during the experimental phase and,
third, the derived numerical models are used to compute a measure of the target SCC
mix robustness based on simulations of mixture parameters. The proposed measure
represents the probability that a SCC mix veriﬁes the fulﬁllment of the acceptance
criteria.
The LABEST/FEUP methodology was ﬁrst developed under the scope of the research
project BACPOR, during the full-scale tests phase of SCC production in Maprel pre-
cast factory, in Rio Maior, Portugal. Later, within the scope of the research project
POCI/ECM/61649/2004 the LABEST/FEUP methodology was applied and compared
with the results given by the methodology suggested in “The European Guidelines for
Self-compacting Concrete” in order to validate it and to verify if it is possible to enhance
robustness of SCC mixtures by only changing proportions of materials in the mixture.
7.2. Robustness deﬁnition
According to Aïtcin et al. (2001) an industrial process is robust if it allows for a large
variation of variable(s) V while maintaining the property(ies) P inside the acceptance
interval(s) (see Figure 7.1). Thus, from Figure 7.1, it can be said that Process A is
more robust than Process B. To attain the same level of robustness with both processes,
it would be necessary to reduce the range of the interval ∆V, in Process B, and this
would only be accomplished by increasing the control on raw materials and/or through
the modernization of existing equipments. In general, these improvements are diﬃcult
191
7. Evaluation of SCC mixtures robustness
Figure 7.1.: Robustness of an industrial process (Aïtcin et al., 2001)
to implement and very expensive so industry systematically tends towards those com-
binations whose level of robustness is high enough to oﬀset the relative lack of quality
control (Aïtcin et al., 2001).
A critical factor in the SCC production process is its sensitivity to small batch-to-batch
changes in one or more of the constituents, which may lead to variability of performance
(BIBM et al., 2005; Bonen et al., 2007; Rigueira et al., 2006, 2007; Concrete Society,
2005). Thus, the concept of concrete “robustness” was introduced, which corresponds
to the tolerance of SCC production, to the daily ﬂuctuations of materials (BIBM et al.,
2005). In “The European Guidelines for Self Compacting Concrete” (BIBM et al., 2005)
the robustness of SCC is deﬁned as the capacity of concrete to retain its fresh properties
when small variations in the properties or quantities of the constituent materials occur.
It should be noted that the European deﬁnition of robustness (BIBM et al., 2005) diﬀers
from the one suggested by the American researchers (Bonen et al., 2007). According
to the ACBM research team (Bonen et al., 2007), robustness is regarded as the ability
of a given mixture to maintain both the fresh properties and uniformity pre- and post-
casting of one batch or successive batches. This is a broader deﬁnition since it includes
the ability of SCC to resist to changes in materials properties and mix proportions,
due to batching inaccuracies, but also the ability to resist to changes during transport
and placement (dynamic stability) and post placement (static stability). According to
Bonen et al. (2007) robustness is a time dependent quality and it should be regarded
according to the application.
The author’s opinion is that, to make it clearer, the concepts of robustness (related to
properties after mixing), dynamic stability (related to properties during transport and
192
7.3. Factors aﬀecting properties during production
casting) and static stability (related to properties from the end of casting to setting time)
should be discussed separately. In the present work, the European deﬁnition was adopted
and generalized as the capacity of concrete to retain its performance requirements, which
is of interest for a speciﬁc application (fresh and hardened properties), when small
variations in the properties or quantities of the constituent materials occur. Thus, the
major concern in this study was the need to reduce intervention at the plant or job site
to adjust the mixtures in successive batches.
7.3. Factors aﬀecting properties during production
During production, there may be a number of factors that individually or collectively
contribute to disturb the balance between SCC fresh state properties, namely, deforma-
bility, passing ability and resistance to segregation. Variations in the SCC properties
can be attributed to variability of the constituent materials (Bonen et al., 2007), in-
accuracy in the weighing of materials (Bonen et al., 2007) and changes in the mixing
energy (Emborg, 2000; Takada, 2004). Changes in the environment conditions, like
temperature and humidity were also found to aﬀect the rheological properties of con-
crete (Sakai et al., 2003). After mixing, during the transport and casting processes
rheological properties of SCC may also suﬀer signiﬁcant changes due to time and tem-
perature eﬀects, shear history eﬀect and thixotropy (Bonen et al., 2007; Roussel, 2007)
but, as mentioned before, transport and casting stages are not covered by the deﬁnition
of robustness adopted here.
7.3.1. Variability of constituent materials
As discussed in paragraph 4.4.2, SCC can be seen as a suspension of aggregates in
paste and the required paste volume depends on the relative proportions of aggregates
in the mixture, particle size distribution of each aggregate type, shape of aggregates,
angularity and texture (de Larrard, 1999; Koehler and Fowler, 2007). Thus, a change in
any of these aggregate characteristics may result in a deviation from the target volume
of paste, by unit volume of concrete. The inﬂuence of ﬁne aggregates on fresh properties
of the SCC is signiﬁcantly greater than that of coarse aggregate (BIBM et al., 2005),
in particular the ﬁner fractions (less than 0,125 mm and 0,09 mm, according to BIBM
et al. (2005) and Okamura et al. (2000), respectively) which should be considered as
ﬁnes accounting directly for the volume of paste. The coarse to ﬁne aggregate ratio in
the mix and the maximum size of aggregates are determining factors for the passing
ability of SCC, for a given reinforcement spacing (BIBM et al., 2005; Billberg, 2002;
Petersson and Billberg, 1999). Changes in the moisture content or water absorption of
193
7. Evaluation of SCC mixtures robustness
aggregates will change the target rheological properties of paste and, consequently, of
concrete. Moisture contents of ﬁne aggregates normally are greater than those of coarse
aggregates.
At paste level, SCC paste can be seen as a suspension of powder particles in water
(Grünewald and Walraven, 2007). Therefore, changes in particle size distribution, shape
and water absorption of ﬁne materials may aﬀect the water demand of SCC (BIBM et al.,
2005). Wallevik et al. (2007) found that changes in cement induced by the production
process can have a signiﬁcant eﬀect on workability and reactions of concrete, especially
with superplasticized mixes. Factors such as ﬁneness, sulfate content, alkalis and C
3
A
content may have a signiﬁcant eﬀect (BIBM et al., 2005; Moir, 2003; Vikan et al., 2007).
The inﬂuence of cement variations, due to the production process, on paste/mortar
properties was assessed and was discussed more in detail in Chapter 6. Admixtures will
normally be very consistent from batch to batch (BIBM et al., 2005), although care
must be taken to keep an uniform concentration of the product inside the storage tank
as well as to consider the eﬀects of seasonal temperature variations. Chemistry and age
of recycled water and solids from slurry may aﬀect workability of SCC (Rilem Technical
Committee, 2006).
A change of the type or source of supply of any of the constituent materials is likely to
make a signiﬁcant change to concrete properties, as it was shown in Chapter 5 for the
cases of a change in the superplasticizer type (same manufacturer) and cement source
(same type).
7.3.2. Inaccuracy in weighing of materials
Deviations in mix proportions can occur due to inaccuracy of weighing equipment which
can aﬀect workability of SCC (Bonen et al., 2007) and, generally, larger errors are
introduced when batching small load sizes. Normal weighing tolerances regulated by
national and international standards are in general acceptable for production of SCC
(Rilem Technical Committee, 2006). Accuracy of admixture dosing equipment may vary
depending on the type of dosing equipment and its set-up (Rilem Technical Committee,
2006). The variation of viscosity of liquid admixtures, due to changes in surrounding
temperature, can also inﬂuence the accuracy of dosing of those admixtures.
7.3.3. Mixing energy
SCC is also less tolerant to changes in the mixing protocol than conventional concrete
because it contains a higher amount of ﬁnes which depend on the mixing energy to be
eﬃciently dispersed (Rilem Technical Committee, 2006). A suitable mixing protocol
194
7.4. Strategies to increase SCC mixtures robustness
is to be established for each individual plant during the initial full-scale tests (Rilem
Technical Committee, 2006). The main inﬂuencing factors of mixing eﬃciency are: the
concrete mixture itself (type of constituent materials and mix-proportions), the loading
sequence, mixing sequence, mixing speed, mixing time, type of mixer, batching volume
and cleanliness of the mixer before loading.
SCC typically requires a longer mixing time or mixing energy than conventional concrete
(Chopin et al., 2004; Rilem Technical Committee, 2006), but it is possible to reduce the
mixing times by increasing the mixing speeds or by changing the conﬁguration of the
paddles in the mixer. Admixture manufacturers recommend that the superplasticizer
should be diluted in water before adding to the concrete, allowing a better dispersion of
the relatively small quantity of admixture within the mass of concrete (Rilem Technical
Committee, 2006). The instant of addition of the superplasticizer may also aﬀect ﬂuid-
ity, depending on the superplasticizer type, as discussed in Chapter 3. Takada (2004)
investigated the eﬀects of the mixer type on fresh properties of SCC and concluded that
forced pan mixers have a higher mixing eﬃciency than drum mixers. Furthermore, it
was shown that the type of superplasticizer used inﬂuences the necessary mixing time
and mixing intensity (Takada, 2004). Chopin et al. (2004) studied the eﬀects of mixing
time by varying the quantity of powder, use of limestone ﬁller, and various types and
contents of silica fume and superplasticizer. These authors showed that the mixing time
could be reduced by increasing the ﬁne particle content (with constant w/c), increasing
the total water amount, increasing maximum solid content of aggregates (for constant
total aggregate content) and replacing part of the cement by silica fume (Chopin et al.,
2004). For better consistency, the volume of the SCC mix should be as near to the
maximum mixer capacity as possible. The mixer should be clean but not dry. Mixing
conventional concrete before mixing SCC may create some inconsistency in properties of
SCC, especially if incompatible admixtures have been used before mixing SCC (Rilem
Technical Committee, 2006).
7.4. Strategies to increase SCC mixtures robustness
Observing Figure 7.1, one can easily conclude that robustness can be increased by
reducing the range of the interval ∆V, which in the case of the SCC production process
can be accomplished by reducing constituent materials variations and deviations from
target mix proportions through more quality control and/or modernization of existing
equipments. Another way to increase robustness is to change from a curve of the type
of Process B to one of the type of Process A (see Figure 7.1), which can be achieved by
a well-balanced selection and proportioning of constituent materials.
195
7. Evaluation of SCC mixtures robustness
7.4.1. Materials selection and mix-proportioning
The constituent materials for SCC are the same as those used in conventional concrete,
conforming to NP EN 206-1 (Portugal. IPQ, 2007), and should satisfy the requirements
for individual constituents covered by speciﬁc European standards (BIBM et al., 2005).
According to BIBM et al. (2005), material selection should try to improve robustness,
making concrete less sensitive to material variability. But, for economic reasons, mate-
rials selection depends much on local availability so there is no ﬁxed rule for the amount
of aggregates, cement, additions and admixtures. Thus, a decisive factor for robustness
of SCC mixtures is a clear understanding of the eﬀect of each constituent material and
of their interaction on SCC properties and an adequate mix-design method (scientiﬁc).
Some indications can be found in the literature to increase robustness of SCC mixtures
through materials selection and mix-proportioning. Bonen et al. (2007) point that the
desired ﬂow properties should be maximized by materials selection rather than increas-
ing superplasticizer content or water content. Khayat et al. (1999b) showed that the
use of coarse aggregate and sand combinations that enable increase in packing density
can reduce superplasticizer demand and plastic viscosity of SCC. Hwang and Khayat
found concrete-equivalent mortars, including naphthalene-based superplasticizer, to be
less sensitive to variations in water content (or more robust) than similar mixtures
including polycarboxylate-based superplasticizer (ACBM, 2007). The type of binder
was also shown to aﬀect robustness (ACBM, 2007). There is some consensus that the
introduction of a viscosity-modifying agent (VA) is eﬀective to minimize the eﬀect of
variations in moisture content, ﬁnes in the sand or its grain size distribution, making
SCC more robust and less sensitive to small variations in the proportions and condi-
tion of other constituents (BIBM et al., 2005) (see Figure 7.2). A VA is an admixture
which is able to modify cohesion of the mixture without signiﬁcantly altering its ﬂuidity.
Sakata et al. (referred by (Bonen et al., 2007)) reported that in SCC made with low
water/powder ratio of 0,33 (powder containing limestone ﬁller), the incorporation of a
small concentration of Welan Gum of 50 g/m
3
can reduce variability in slump ﬂow of
SCC due to changes in cement Blaine (318 to 342 m
2
/kg), ﬁneness modulus of sand
(2,08 to 3,06), and temperature of fresh concrete (10 to 30
◦
C). But according to Khayat
et al. (1999c) SCC made with a low content of VA and a relatively low water content
can represent greater robustness than SCC made with a low binder content and a higher
dosage of VA. In the former mixtures, VA is used to reduce variation in rheology due to
some small changes in the materials properties, while yield stress and viscosity values
are mainly controlled by low w/c and aggregate/cement. The latter mixtures are less
robust because any small changes in VA content compromise viscosity and can lead
to excessive bleeding and segregation. This means that VA should not be regarded as
a way of avoiding the need for a good mix design and careful selection of other SCC
196
7.4. Strategies to increase SCC mixtures robustness
Figure 7.2.: Illustration of the eﬀect of a viscosity agent on SCC robustness (Shindoh
and Matsuoka, 2003)
constituents.
7.4.2. Quality control
Experience shows that SCC can be successfully produced in a consistent and continuous
way only at a properly equipped concrete mixing plant under an established and reliable
quality assurance system (Rilem Technical Committee, 2006). It is recommended (and it
is a requirement in some EU member countries) that the producer is qualiﬁed according
to ISO 9001 or equivalent (BIBM et al., 2005). Depending on the mixture’s robustness,
the control of the constituent materials needs to be increased and the tolerable variations
restricted, so that daily production of SCC is within the conformity criteria without the
need to test and/or adjust every batch.
In general, it is recommended that the aggregates are evaluated each production day
prior to commencing batching. Thereafter, visual checks should be carried out on each
delivery of aggregate; any noticeable change should be evaluated prior to accepting or
rejecting the delivery. The moisture content of aggregates should be continuously mon-
itored and the mix adjusted to account for any variation. When new batches of cement,
addition or admixture are delivered, additional performance tests may be necessary to
monitor any signiﬁcant changes or interactions between constituents. Batching equip-
ment should be regularly checked for its accuracy. An investment in modernization of
existing equipment, increasing its accuracy, will eventually produce more economical
SCC.
It is important that all personnel who will be involved in the production and delivery
of SCC receive adequate training prior to production from a person with previous ex-
197
7. Evaluation of SCC mixtures robustness
perience of self-compacting concrete. This training may include observing trial batches
being produced and tested.
7.5. Robustness evaluation methods
In “The European Guidelines for Self-Compacting Concrete” the robustness checking
is recognized as an important step in the SCC design process (BIBM et al., 2005).
Since variability of most constituent materials can be translated by a change in water
requirement, it is suggested by BIBM et al. (2005) that compositions with plus and
minus 5 to 10 litres of the target water content be tested and the respective changes in
fresh state properties be measured. A robust SCC should tolerate these deviations, i.e.
should maintain its fresh properties inside the speciﬁed limits (BIBM et al., 2005).
Hwang and Khayat (referred in (Bonen et al., 2007)) suggested determining an in-
dex of mixtures’ robustness, the minimum water content (MWC), by testing concrete-
equivalent mortars. MWC was determined as the increase in w/c that can lead to a unit
change of ﬂow diameter. Combinations of materials leading to higher values of MWC,
that can result in a lower degree of increase of ﬂow diameter after a given increase of
w/c, are the most robust combinations. Nachbaur et al. (2005) also suggested the exten-
sion of “concrete equivalent mortar” method (Schwartzentruber and Catherine, 2000)
to SCC, to predict the SCC mixture properties based on results obtained in equiva-
lent mortars reducing, this way, the amount of concrete batches needed to complete
the mixture proportioning optimization. Grünewald and Walraven (2007) introduced a
variation of ±10 l/m
3
on reference pastes and concluded that the information obtained
from the mini-slump test is not very accurate to identify the robustness of SCC mixtures
from the paste test alone.
These approaches, based only on a change in water content, seem too simplistic because
they do not take into account the speciﬁc characteristics of the production centre, like
the existing level of quality control, equipment performance, skills and knowledge of
the personnel involved. Therefore, in the present study a methodology was developed
to compute a robustness measure based on data of typical materials’ weight deviations
inherent to the production process, at a speciﬁc production centre. This methodology
is presented in the next section.
7.6. LABEST/FEUP robustness evaluation method
The present study was developed under the scope of the research project BACPOR,
during the full-scale tests phase of SCC production in Maprel precast factory, in Rio
198
7.6. LABEST/FEUP robustness evaluation method
Table 7.1.: Grading of aggregates (BACPOR, Maprel, Rio Maior)
Sieve
size (mm) 0,074 0,150 0,297 0,59 1,18 2,38 4,75 6,30 9,5 12,5
sand 1 0,0 5,3 19,2 48,1 81,5 98,9 100 100 100 100
sand 2 0,9 4,5 14,7 38,7 66,6 88,1 99,9 100 100 100
coarse aggregate 0,0 0,11 0,5 0,7 1,0 2,3 20,7 46,3 90,5 100
Maior, Portugal. The objective of this study was to evaluate the robustness of the
target SCC mix composition, to be applied during full-scale tests, based on information
of daily ﬂuctuations inherent from the production process at Maprel precast factory.
The corresponding experimental plan was carried out between 8
th
and 14
th
of March
2005.
7.6.1. Experimental programme
Materials characterization
Crushed calcareous aggregate (1-12 mm), a siliceous natural ﬁne sand (sand 1) with a
ﬁneness modulus of 2,47 and natural coarse sand (sand 2) with a ﬁneness modulus of
2,87 were used (see Table 7.1). The speciﬁc gravity of the coarse aggregate, sand 1 and
sand 2 were 2,61, 2,54 and 2,54, and absorption values were 1,31%, 1,10% and 0,96%,
respectively, according to NP EN 1097-6 (Portugal. IPQ, 2004). In this study SCC mix
was prepared with Portland cement (CEM I 52,5 R) and a mineral addition (limestone
ﬁller), with a speciﬁc gravity of 3,12 and 2,70, respectively. A polycarboxylate type
superplasticizer was used having a speciﬁc gravity of 1,05 and 20,2% solid content.
Experimental design
A 2
(5−1)
fractional factorial statistical design (Montgomery, 2001), corresponding to ﬁve
parameters at two levels, was used to establish models that describe key SCC properties.
Since the central point in the factorial design applied in this study corresponds to a
SCC mixture optimized in a previous study (Nunes et al., 2005c), the analysed region is
relatively close to the optimum. In this situation a second order model is usually required
to approximate the response because of the curvature that may be present in the true
response surface. For this reason the factorial design (2
4
=16 runs) was augmented with
10 axial runs plus 4 central runs, resulting in a central composite design that can be
used to ﬁt a second-order model (Montgomery, 2001).
SCC mix proportions can be established based on the following parameters: water to
powder volume ratio (V
w
/V
p
); ﬁller to cement weight ratio (w
f
/w
c
); superplasticizer to
199
7. Evaluation of SCC mixtures robustness
Table 7.2.: Coded values for the factors used in the experimental design
Ref. point type V
w
/V
p
w
f
/w
c
Sp/p s
1
/s V
g
/V
g,lim
Ci
a
central 0 0 0 0 0
F1 factorial -1 -1 -1 -1 1
F2 1 -1 -1 -1 -1
F3 -1 1 -1 -1 -1
F4 1 1 -1 -1 1
F5 -1 -1 1 -1 -1
F6 1 -1 1 -1 1
F7 -1 1 1 -1 1
F8 1 1 1 -1 -1
F9 -1 -1 -1 1 -1
F10 1 -1 -1 1 1
F11 -1 1 -1 1 1
F12 1 1 -1 1 -1
F13 -1 -1 1 1 1
F14 1 -1 1 1 -1
F15 -1 1 1 1 -1
F16 1 1 1 1 1
CC1 axial 2 0 0 0 0
CC2 -2 0 0 0 0
CC3 0 2 0 0 0
CC4 0 -2 0 0 0
CC5 0 0 2 0 0
CC6 0 0 -2 0 0
CC7 0 0 0 2 0
CC8 0 0 0 -2 0
CC9 0 0 0 0 2
CC10 0 0 0 0 -2
a
the central point was replicated four times (i=1 to 4)
powder weight ratio (Sp/p); sand to mortar volume (V
s
/V
m
); solid volume (V
g
/V
g,lim
),
as suggested by Okamura et al. (2000). An additional parameter must be considered
when ﬁne aggregate is a combination of two sands. In this work weight ratio (s
1
/s) sand
1 to total sand was used, resulting in ﬁve factors used in the modelling: V
w
/V
p
; w
f
/w
c
;
Sp/p; s
1
/s and V
g
/V
g,lim
. The volumetric ratio V
s
/V
m
was kept constant and equal to
0,462. The eﬀect of each factor was evaluated at ﬁve diﬀerent levels −α, –1, 0, +1, +α
as presented in Table 7.2 and the design was made rotatable by taking α equal to 2,0.
Actual parameter values, at each level, are given in Table 7.3.
200
7.6. LABEST/FEUP robustness evaluation method
Table 7.3.: Correspondence between coded values and actual parameter values
mixture parameter -2 -1 0 +1 +2
V
w
/V
p
0,727 0,791 0,855 0,919 0,983
w
f
/w
c
0,432 0,470 0,508 0,546 0,584
Sp/p 0,020 0,021 0,023 0,025 0,026
s
1
/s 0,598 0,673 0,748 0,823 0,897
Mixing sequence, testing methods and test results
Mixtures from the experimental plan were tested in a random order. The mixes were
prepared in the laboratory in 25 litres batches and mixed in an open pan mixer. The
mixing sequence consisted of mixing both sands and coarse aggregate with
1
/4 of the
mixing water during 2
1
/2 minutes, waiting for 2
1
/2 minutes for absorption, adding powder
materials, followed by the rest of the water with the superplasticizer and ﬁnally mixing
concrete during a further 8 minutes. Slump-ﬂow, V-funnel and Box tests were then
carried out to characterize fresh state. Details on equipment used for testing fresh
concrete and on test procedures can be found in (BIBM et al., 2005). After tests on fresh
concrete, three standard 150 mm cubes were moulded to evaluate 28 days compressive
strength (fc,28). Concrete cubes were demoulded one day after casting and kept inside
a chamber under controlled environmental conditions (Temperature=20
◦
C and HR=95-
98%) until a compressive strength test was carried out at 28 days concrete age. The
Slump-ﬂow test was used to evaluate deformation capacity, viscosity and resistance to
segregation of SCC (by visual observation). From this test, ﬁnal slump ﬂow diameter
(Dﬂow) and time necessary for concrete to reach a 50 cm diameter (T50) were recorded.
The V-funnel test was used to assess viscosity and passing ability of SCC. Test ﬂow time
was recorded (Tfunnel). The Box test was used to assess the ability of concrete to pass
through tight openings between reinforcing bars and ﬁlling ability; ﬁlling height was
recorded (H). Dﬂow, T50, Tfunnel, H and fc,28 were the selected concrete properties to
be analysed and modelled.
The mix proportions and test results of the 30 mixes prepared as described above are
summarized in Tables E.15 and E.16 in Appendix E. From these results it may be
observed that with this experimental plan a wide range of SCCs was covered with Dﬂow
ranging from 505 to 750 mm, T50 ranging from 1,47 to 6,31 s, Tfunnel ranging from
6,59 to 28,47 s and fc,28 ranging from 52,20 to 75,31 MPa. All mixes exhibited a ﬁlling
height in the Box-test higher than 300 mm, meaning that blocking is not a critical aspect
in the analysed region; this can be attributed to low maximum aggregate size and low
coarse aggregate content. None of the mixes exhibited severe segregation.
201
7. Evaluation of SCC mixtures robustness
Table 7.4.: Fitted numerical models (coded variables)
Response Dﬂow T50 [Tfunnel (s)]
−0,5
H fc,28
variable (mm) (s) (mm) (MPa)
model terms estimate
independent 650,53 2,936 0,304 333,36 63,00
V
w
/V
p
58,67 -1,291 0,054 8,12 -3,29
w
f
/w
c
8,21 -0,110 0,006 NS -1,11
Sp/p 23,5 -0,355 NS 2,79 1,25
s
1
/s NS -0,035 0,004 NS 1,26
V
g
/V
g,lim
-8,46 0,220 NS -3,54 -0,67
(w
f
/w
c
)×(s
1
/s) NS 0,344 -0,009 NS NS
(Sp/p)×(V
g
/V
g,lim
) 10,44 -0,451 NS 3,44 -1,24
(V
w
/V
p
)
2
-6,95 0,316 NS -3,95 NS
(V
g
/V
g,lim
)
2
NS NS NS NS -1,46
residual error, ε
a
mean 0 0 0 0 0
standard deviation 13,188 0,468 0,015
b
5,023 2,248
R
2
0,953 0,865 0,921 0,787 0,759
R
2
adj
0,941 0,811 0,909 0,743 0,682
(NS) non-signiﬁcant terms;
a
error term is a random and normally distributed variable;
b
corresponding value for Tfunnel is 4,30
7.6.2. Response models
In this work, commercial software (Design-Expert) (State-Ease Corporation, 2000) was
used to analyse results for each response variable by examining summary plots of the
data, ﬁtting a model using regression analysis and ANOVA, validating the model by
examining the residuals for trends and outliers and interpreting the model graphically.
Fitted models
The estimates of the ﬁtted models, including the residual error term, along with the
correlation coeﬃcients, are given in Table 7.4. An analysis of variance showed that
these models are signiﬁcant when describing the eﬀect of V
w
/V
p
, w
f
/w
c
, Sp/p, s
1
/s and
V
g
/V
g,lim
on the modelled responses. Residual analysis did not reveal any obvious model
inadequacies or indicate serious violations of the normality assumptions, except in the
case of Tfunnel. This problem was overcome after a variable transformation of the type
1/
√
y, as indicated in Table 7.4.
202
7.6. LABEST/FEUP robustness evaluation method
Table 7.5.: Coded values and test results from a previous study
Dﬂow T50 Tfunnel H fc,28
V
w
/V
p
w
f
/w
c
Sp/p s
1
/s V
g
/V
g,lim
Date (mm) (s) (s) (mm) (MPa)
0 0 0 0 0 14-02-2005 645,0 2,78 10,07 335 69,03
0 0 0 0 0 14-02-2005 630,0 3,10 10,69 320 69,17
0 0 0 0 0 14-02-2005 683,0 2,59 9,44 322 –
0 0 0 0 0 15-02-2005 670,0 2,72 10,09 318 62,82
1 0 0 0 0 15-02-2005 705,0 2,57 7,85 340 65,20
-1 0 0 0 0 15-02-2005 610,0 3,97 13,47 330 68,90
0 0 -1 0 0 15-02-2005 645,0 2,47 10,43 332 61,85
0 0 1 0 0 15-02-2005 697,5 2,66 10,72 325 66,03
Table 7.6.: Statistics of the results for the central points
Central Dﬂow T50 Tfunnel H fc,28
points (n=8) (mm) (s) (s) (mm) (MPa)
mean 656 2,8 10,1 328 64,6
standard deviation 17 0,2 0,6 9 3,2
coeﬃcient of variation 2,6% 8,1% 5,7% 2,8% 5,0%
estimated error
a
±11,6 ±0,15 ±0,40 ±6,3 ±2,4
a
corresponding to a 95% conﬁdence level
Accuracy of the proposed models
Even though the majority of the ﬁtted models exhibited relatively high correlation
coeﬃcients (see R
2
and R
2
adj
in Table 7.4) their accuracy must be veriﬁed. The results
of four central points included in the experimental design, together with four additional
central runs from a previous study (see data in Table 7.5) were analysed in order to
estimate the experimental error. The corresponding mean value, standard deviation,
coeﬃcient of variation and estimated error (95% conﬁdence interval) are presented in
Table 7.6. The lowest coeﬃcient of variation (2,6%) was associated to Dﬂow and the
highest one (8,1%) was associated to T50. As can be observed in Table E.15 from
Appendix E and Table 7.5 replicate runs of central points were spread out in time to
get a rough check on the stability of the process during the experimental programme.
The accuracy of the derived models can be assessed by comparing the residual standard
deviation (see Table 7.4) and the standard deviation calculated from the central points
(see Table 7.6)(Montgomery, 2001). A good ﬁt can be expected when the residual
standard deviation does not exceed the experimental error by far. In this study, the
standard deviation measured on the central points was always higher or close to the
residual standard deviation, except in the case of T50.
The accuracy of the proposed models was also analysed by comparing predicted-to-
203
7. Evaluation of SCC mixtures robustness
Figure 7.3.: Comparison of measured versus predicted values of Dﬂow
measured values obtained with the eight mixtures presented in Table 7.5. The ratio
between predicted-to-measured values for Dﬂow, T50, Tfunnel, H and fc,28 ranged
between 0,95 and 1,03, 0,76 and 1,33, 0,99 and 1,18, 0,97 and 1,05, 0,91 and 1,02,
respectively. Again, these values indicate good accuracy for the established models
except in the case of T50. The predicted-to-measured values of Dﬂow, T50, Tfunnel,
H and fc,28 are shown in Figures 7.3 to 7.7, respectively, with the prediction intervals
corresponding to a 95% conﬁdence level. In these ﬁgures the black dots represent the
experimental design results and the white dots represent results obtained in a previous
study (not used to derive the numerical models). One can observe that all points fall
within or very close to the limits of the prediction intervals. Thus, one can expect the
established models to be suﬃciently accurate to predict the analysed fresh and hardened
properties.
Individual and interaction eﬀects
Since statistical models were established in coded variables, the estimates of the model
coeﬃcients presented in Table 7.4 give an indication of the relative signiﬁcance of the
various mixture parameters on each response. Naturally a negative coeﬃcient means
that the response variable will decrease if the given mixture parameter increases. The
results in Table 7.4 clearly show that V
w
/V
p
exhibit the greatest eﬀect on all ﬁve
measured responses. The variables Sp/p and V
g
/V
g,lim
also inﬂuence SCC properties.
Signiﬁcant interaction eﬀects were found between w
f
/w
c
and s
1
/s on both T50 and
(Tfunnel
−0,5
) responses and between Sp/p and V
g
/V
g,lim
on all the analyzed responses
except (Tfunnel
−0,5
). The quadratic term in V
w
/V
p
was signiﬁcant for Dﬂow, T50 and
H responses. The quadratic term in V
g
/V
g,lim
was signiﬁcant for the fc,28 response.
204
7.6. LABEST/FEUP robustness evaluation method
Figure 7.4.: Comparison of measured versus predicted values of T50
Figure 7.5.: Comparison of measured versus predicted values of Tfunnel
205
7. Evaluation of SCC mixtures robustness
Figure 7.6.: Comparison of measured versus predicted values of H
Figure 7.7.: Comparison of measured versus predicted values of fc,28
206
7.6. LABEST/FEUP robustness evaluation method
7.6.3. Robustness measure
When adapting Figure 7.1 to the case of concrete production process, a large number of
independent variables (X
i
, i = 1, . . . , n) inﬂuence concrete properties (Y
i
, i = 1, . . . , n)
and a considerable amount of concrete properties must be assessed to verify the ful-
ﬁlment of the performance criteria. In the present study, as mentioned before, ﬁve
independent variables were considered, namely, X
1
= V
w
/V
p
, X
2
= w
f
/w
c
, X
3
= Sp/p,
X
4
= s
1
/s and X
5
= V
g
/V
g,lim
. Based on these variables numerical models were derived
to describe Y
1
=Dﬂow, Y
2
=T50, Y
3
=Tfunnel, Y
4
=H and Y
5
=fc,28, which are the depen-
dent variables. Once the acceptance limits for each dependent variable are established,
if the typical ﬂuctuations of V
w
/V
p
, w
f
/w
c
, Sp/p, s
1
/s and V
g
/V
g,lim
associated to the
production process have a known distribution one can consider the probability that the
performance criteria is fulﬁlled, given by
p = P

5
¸
i=1
R
i
inf
< Y
i
< R
i
sup

(7.1)
as a measure of robustness of the SCC mix. Since this probability cannot be computed
exactly, one can use the frequency of accepted intervals computed on a large sample
f =
number of occurences of the event ”

¸
5
i=1
R
i
inf
< Y
i
< R
i
sup

”
sample size
(7.2)
to estimate 7.1, that is, to estimate robustness of the SCC mix under study. As it
will be explained later in this section, applying bootstrap resampling techniques enables
improvement of this estimate and evaluating how accurately a statistic calculated from
the observed data estimates the corresponding quantity for the whole population (Efron
and Tibshirani, 1993).
Constituent materials variations
Since this study was carried out prior to the industrial application of SCC at Maprel
precast factory there was no available data of daily ﬂuctuations concerning SCC pro-
duction. Therefore, records of target and measured weight values of each constituent
material corresponding to 1,5 m
3
batches of conventional concrete (C45/55) were used.
These batches were all produced at Maprel precast factory during one week using the
same materials that were used for the experimental factorial plan. A total of 132 ob-
servations were collected. Figure 7.8 presents the observed deviations of the constituent
materials. The deviations were calculated dividing the diﬀerence between the target
and the measured values by the target value. Absolute deviations for aggregates were
added up and presented as “aggregates”. For the superplasticizer, no deviations were
207
7. Evaluation of SCC mixtures robustness
Figure 7.8.: Observed deviations of constituent materials during one week of concrete
production in Maprel precast factory
recorded. It may be observed from Figure 7.8 that deviations respect the limits imposed
in the NP EN 206-1 (Portugal. IPQ, 2007). Some deviations remained a constant sign
(positive in the cases of water, sand 1 and coarse aggregate and negative in the case of
sand 2) along the production process when both positive and negative deviations would
be expected to occur randomly.
Bootstrap re-sampling
A bootstrap sample is obtained by randomly sampling n times, with replacement, from
the original N data points. The sampling process consists of randomly generating
j
1
, j
2
, . . . , j
n
integers, each of which equals any value between 1 and N with probability
1/N. These integers determine which members of the original data set are selected to
be in the random sample. This process allows a simple member to appear once, more
than once or never. For each bootstrap sample (of size n) the statistic of interest can
be evaluated and it is called a bootstrap replication. This process is repeated many
times to generate B bootstrap samples and respective bootstrap replications. Then
the sample of the bootstrap replications (size B) is used to assess the accuracy of the
computed statistic, for example, to construct conﬁdence intervals (Efron and Tibshirani,
1993). Applying the ﬁrst percentile method (Efron and Tibshirani, 1993) the 100(1-α)%
conﬁdence interval for the true value of the unknown parameter of the population is
then given by the two values that include the central 100(1-α)% of this distribution.
For example a 95% conﬁdence interval is given by the 2,5% and 97,5 % percentiles of
the generated distribution. Details on this statistical method are reported in (Efron and
208
7.6. LABEST/FEUP robustness evaluation method
Figure 7.9.: Bootstrap re-sampling to compute a robustness measure
Tibshirani, 1993).
In the present study the quantity to be estimated is the probability p given in equa-
tion (7.1) and the frequency of accepted intervals given in equation (7.2) is computed
for each bootstrap sample. An improved estimate of p, i.e. the robustness measure, is
given by the mean of the frequencies obtained in the B replications (see Figure 7.9).
Implementation, results and discussion
In this work, as previously mentioned, N = 132 independent data points were observed,
consisting of target and measured weight values of each constituent material. From this
data, a large number of independent bootstrap samples (B = 2000) were generated,
each one of size n = 100. For each re-sampled point in the bootstrap sample, the target
and measured values of independent variables (X
1
= V
w
/V
p
; X
2
= w
f
/w
c
; X
3
= Sp/p;
X
4
= s
1
/s and X
5
= V
g
/V
g,lim
) were calculated from the weight values of each con-
stituent material. Then, the corresponding deviation ∆X
i
from the target value in each
independent variable was calculated
∆X
i
= X
measured
i
−X
target
i
(7.3)
These deviations where added to the values of the independent variables of the target
SCC mix (corresponding to the central point in the factorial design, X
0
i
) to simulate
the variations associated to the production process
X
i
= X
0
i
+ ∆X
i
(7.4)
209
7. Evaluation of SCC mixtures robustness
Table 7.7.: Descriptive statistics of bootstrap samples
p
1
p
2
p
3
p
4
p
5
p
number of bootstrap
replications (B) 2000 2000 2000 2000 2000 2000
mean 0,965 0,965 0,918 1 0,864 0,739
median 0,970 0,970 0,920 1 0,870 0,740
mode 0,970 0,970 0,920 1 0,860 0,740
std. error of mean 3,99× 10
−4
4,06× 10
−4
6,21× 10
−4
0 7,63× 10
−4
9,76× 10
−4
minimum 0,900 0,880 0,800 1 0,730 0,590
maximum 1,000 1,000 0,990 1 0,960 0,890
std. deviation 0,018 0,018 0,028 0 0,034 0,044
percentile 2,5% 0,930 0,930 0,860 1 0,790 0,650
percentile 97,5% 0,990 0,990 0,970 1 0,930 0,820
Finally, concrete properties (Y
1
=Dﬂow; Y
2
=T50; Y
3
=Tfunnel; Y
4
=H and Y
5
=fc,28)
could be estimated using the derived numerical models presented in Table 7.4. The
statistic presented in equation (7.2) (a frequency) was computed for each bootstrap
sample, as an estimate of the target SCC mixture robustness. Besides this frequency,
individual frequencies were also computed as estimates of
p
i
= P